25 results about "Classifier fusion" patented technology

The invention discloses a radar interference identification method based on multi-representation domain feature interactive fusion and a graph structure semantic classifier, and the method comprises the steps: constructing a multi-representation domain feature interactive fusion module, taking a time domain waveform, a spectrogram and a time-frequency spectrogram as input data, and outputting fusion features; constructing a graph structure semantic classifier, inputting the fused features into the graph structure semantic classifier, analyzing semantic association of different types of signals by the graph structure semantic classifier, and outputting a classification result; dividing the data set into a training set and a test set, and storing network parameters with the best classification effect in the training set according to a classification result; and loading network parameters, and inputting the test set into the radar interference identification network for processing to obtain an identification result. According to the method, the interference signals can be fully represented, the identification and processing capability of the interference signals is improved, robustness representation is carried out on the interference signals, and the accuracy of radar interference identification is improved.

The invention provides a corn variety identification method based on artificial intelligence, and the method comprises the steps: obtaining multi-source feature data of a target corn, the multi-source feature data at least comprising image feature data, spectral feature data and environment feature data; respectively preprocessing the multi-source feature data, and inputting a corresponding pre-training model to extract a multi-source feature vector of the target corn; determining a weight coefficient for feature fusion according to a multi-source feature contribution degree dynamic evaluation mechanism; performing weighted fusion on the multi-source feature vector based on the weight coefficient to obtain an enhanced feature vector; and performing multi-scale attention weighting and integrated classifier fusion processing on the enhanced feature vector to output a variety identification result of the target corn. According to the method, subjective dependence is reduced, the recognition efficiency is improved, and the method has relatively high precision and robustness.

The invention relates to the technical field of nuclear power plants, in particular to a nuclear power equipment operation defect grading method and device, electronic equipment and a storage medium. The invention provides a nuclear power equipment operation defect grading method, and aims to realize grading of nuclear power equipment operation defects through a systematic and multi-level fusion decision-making mechanism. The problems that rule grading lacks flexibility, graph grading is influenced by keyword sensitivity, large model grading field knowledge is insufficient, and precision and adaptability are limited due to the fact that a traditional fusion strategy is single in the prior art are effectively solved. The core of the method is that lexical meta processing and a value matrix mechanism are introduced, dynamic and fine-grained fusion of multi-base classifier output is achieved, and therefore the overall performance of a grading system is improved. Through lexical element processing, value matrix construction and dynamic updating and multi-classifier fusion decision making, the nuclear power equipment defect grading method with high precision, high adaptability and high reliability is realized, and many limitations in related technologies are effectively solved.

The invention provides an underground drilling working condition identification method and device based on data driving. The method comprises the following steps: before underground drilling, introducing contribution values to quantify the importance of different working condition characteristics, and carrying out interpretability analysis on the output of a working condition classifier of each working condition; a judgment matrix of each working condition is constructed according to contribution values of different working condition characteristics to each working condition, so that a weight vector of each working condition is obtained, and a data-driven objective weight distribution method is established; during underground drilling, underground measurement while drilling data are collected, and underground drilling parameter data can be directly obtained; according to the weight vector of each working condition and the working condition index value of each working condition, the working condition of each window is decided through multi-classifier fusion; and when a preset number of different working conditions are accumulatively output every time, updating the feature vector of each window and the working condition index threshold value of each working condition, and establishing a dual dynamic updating strategy pair to adapt to a complex and changeable drilling environment. According to the method, the accuracy and the real-time performance of working condition identification are improved.

The invention discloses an industrial process unknown fault diagnosis method based on a probability random forest, which belongs to the technical field of industrial process fault diagnosis, and realizes fault diagnosis by extracting potential fault features through a variation encoder, constructing a fault classifier based on the probability random forest and fusing a discriminator of an entropy and extreme value theory. According to the method, process variable data with the sampling frequency of 3 minutes and available fault labels are used for analysis, feature extraction is carried out through a variational encoder, and robust potential features with specific distribution are extracted; designing a fault classifier based on a probability random forest, classifying fault samples of known categories, and feeding classification loss of the classifier back to an encoder to adjust distribution of potential features so as to optimize extraction and classification performance of the model on fault features; a fault diagnosis method fusing entropy and extreme value theories is designed, unknown fault diagnosis in the industrial process is completed, and accurate recognition and efficient distinguishing of fault modes in the complex industrial process are achieved.

A circuit breaker closing vibration signal state evaluation method and system based on multi-feature extraction and multi-classifier fusion are characterized in that the method comprises the following steps: extracting closing signal features from three dimensions of a time domain, a frequency domain and a time-frequency domain, and comprehensively applying a plurality of feature selection methods to calculate feature importance scores; determining the number of finally selected features according to cumulative distribution of feature importance, and performing fault classification on the finally selected features by adopting a multi-classifier optimization integration framework; a high-dimensional feature space is reduced to a 2D or 3D space through a t-SNE technology, and space division of different fault types is realized.

The invention relates to a transformer light gas fault detection method and device based on deep learning, and belongs to the field of transformer fault diagnosis. An integrated classification model based on an improved whale optimization algorithm and adaptive enhancement is constructed, support vector machine hyper-parameters are optimized through Tent chaotic mapping initialization, a Levy flight strategy and a simulated annealing mechanism, and model stability and classification precision are improved in combination with weighted multi-weak classifier fusion and a regularization technology; the method comprises the following steps: collecting light gas data by adopting multiple types of gas sensors, and carrying out data preprocessing and feature dimension reduction by utilizing standardization, principal component analysis and linear discriminant analysis; real-time fault diagnosis and feedback are realized based on a gas concentration and fault type relation formula and an edge computing node, and the timeliness and accuracy of fault diagnosis are improved; the method has high real-time performance and practicability, and can quickly complete detection and fault identification of light gas fault gas of the transformer.

The present invention discloses a method for predicting steel breakout based on stacking multi-classifier fusion, belonging to the field of iron and steel metallurgy. The method comprises the following steps: constructing a sample library of true and false steel breakouts; extracting temperature characteristic data and time-series temperature rates of true and false steel breakouts; constructing feature vectors of true and false steel breakout samples; preprocessing the true and false steel breakout feature data; constructing a steel breakout prediction model based on stacking multi-classifier fusion using random forest, K-nearest neighbor classification, and support vector classification as primary classifiers, and logistic regression as a secondary classifier; and finally determining an optimal threshold point through a receiver operating characteristic curve. If the threshold is greater than the optimal threshold, the prediction is judged as steel breakout. The present invention combines the steel breakout temperature characteristic vector with a multi-classifier fusion recognition method to establish a strong classification and recognition model for steel breakouts. While ensuring a 100% steel breakout reporting rate, the method reduces the false alarm rate and improves the prediction accuracy.

The invention discloses a customer product personalized recommendation method and device based on a multi-classifier fusion model and computer equipment, and the method comprises the steps: extracting effective features from the original feature data of multiple dimensions in a preset customer range in a set historical time period through a self-encoding algorithm based on deep learning; a model training sample of the target customer group is formed, and the model training efficiency is improved. According to the method, a stack generalization integrated learning training strategy is utilized, and a plurality of machine learning models are fused, so that the prediction accuracy of the recommendation model obtained by training is greatly improved, and then business personnel can accurately judge the product preference of a customer, adopt a personalized marketing strategy to cope with the customer demand and improve the sales success rate.

The application provides a car data document classification method and device, equipment and medium, relates to the technical field of data document classification, and obtains a text feature vector, a similarity feature vector and an initial classification feature vector corresponding to a to-be-classified car data document; generates a fusion classification feature vector corresponding to the to-be-classified car data document according to the text feature vector, the similarity feature vector and the initial classification feature vector; inputs the fusion classification feature vector and the similarity feature vector into a preset document classification model obtained by training a multi-Fisher classifier according to the fusion classification feature vectors of historical car data documents and corresponding classification labels; and obtains a classification result of the to-be-classified car data document according to an output result of the preset document classification model. The car data document classification model of the multi-classifier fusion is constructed, so that the generalization ability of the traditional classification model is improved, and the classification precision and efficiency are improved.

The embodiment of the invention provides a generative classifier robust retraining method and system for medical federated learning, and relates to the technical field of machine learning, and the method comprises the steps: each participating client generates an adversarial network based on local data training federated conditions, learns global data distribution, and generates intermediate layer features; implementing a knowledge transfer mechanism, and transferring the feature extraction capability of the global classification model to a local discriminator; generating an adversarial network based on the trained federal conditions, and generating a class-balanced synthetic feature data set at a server side; and a feature fixed retraining strategy is adopted, and only the classifier part of the global model is retrained. According to the method, a federated condition generative adversarial network is utilized at a server side to generate class balanced intermediate features, a classifier specially used for retraining a global model is adopted, and the condition generative adversarial network and a knowledge transfer mechanism are fused, so that high-quality balanced feature representation can be generated according to global data distribution; and the problems of non-independent identical distribution and long-tail data distribution are effectively solved.

A direct current partial discharge signal time domain feature classification method, system and device based on ensemble learning and a medium, the method comprises the following steps: preprocessing a partial discharge original signal, and dividing the partial discharge original signal into a training set and a test set according to a preset proportion; using a voting classifier to fuse the limit gradient lifting, the random forest and the support vector machine into an integrated model through a soft voting strategy; inputting the training set into the integrated model for training, and performing performance evaluation on the trained integrated model by using the test set; preprocessing a to-be-detected partial discharge original signal, inputting the to-be-detected partial discharge original signal into the trained integrated model, and obtaining a classification result in combination with a soft voting strategy; a training set and a test set with high signal-to-noise ratio and high feature representation capability are constructed through preprocessing, and the training set and the test set are utilized to perform training and performance evaluation on the integrated model fused by the voting classifier, so that the integrated model can accurately predict classification results of multiple types of typical insulation defects of the to-be-detected partial discharge original signals.

The invention discloses an enhanced EedACA method for prototype aggregation and classifier fusion in heterogeneous federated learning, and relates to an enhanced FedACA method. The invention aims to solve the problems of insufficient prototype expression, insufficient classifier aggregation strategy and weak individuation ability in the current heterogeneous federal learning. On the premise of ensuring the privacy of the client, the adaptive capacity of the model to the non-independent identically distributed data can be improved, the robustness and generalization ability of the global model under the conditions of data isomerism and model isomerism can be enhanced, and the method is particularly suitable for scenes such as intelligent terminal collaborative modeling, personalized recommendation and edge calculation. The invention belongs to the technical field of artificial intelligence.

The present invention provides a method for detecting abnormal users in a social network based on D-S evidence theory fusion. The method includes: constructing and training a convolutional neural network classification model and a K-nearest neighbor algorithm classification model to obtain the accuracy rates of the two classification models for detecting abnormal users; respectively using the two classification models to identify the blog text of the user to be detected to obtain the detection results of the two classification models for the user to be detected; based on the accuracy rates of the two classification models for detecting abnormal users through the D-S fusion rule, fusing the detection results of the convolutional neural network classification model and the K-nearest neighbor algorithm classification model for the user to be detected to obtain the detection result of the abnormal user of the user to be detected. The present invention combines the recognition results and classification accuracy rates of the content to be detected on each classifier, and uses the D-S evidence theory fusion rule to identify the user to be detected after classifier fusion, realizing the detection of abnormal Weibo users in a balanced and effective manner.

The application provides a multi-classifier fusion method based on a quantum convolutional neural network, aiming at the problems of large deep learning computing resource demand and poor multi-classifier fusion effect, three quantum convolutional neural networks with different convolutional structures are proposed as base classifiers based on a parameterized quantum circuit by considering entanglement ability and expressibility of the circuit, so that the required quantum bits and training parameters are reduced, and high classification accuracy and certain difference are simultaneously achieved; the output results of the base classifiers are converted into evidence forms, and an improved quantum average combination method is used as a fusion method of the multi-quantum classifier, so that conflicts among the base classifiers are solved, and accurate classification of the multi-classifier fusion system is realized.

The invention provides a rolling bearing small sample open set cross-working-condition fault diagnosis method based on graph learning, and relates to the technical field of rotating machine fault diagnosis. According to the invention, through constructing a dynamic adaptive graph convolution multi-classifier fusion network, designing an unknown fault category discrimination joint strategy and adopting an N-way K-shot element learning mode, the model can realize effective identification of known fault types across working conditions and can effectively identify unknown fault types under the constraint of small samples, so that the method has the advantages of high efficiency and high reliability. Therefore, the accuracy of fault diagnosis of the rolling bearing is improved.

This invention provides an ECG signal classification system based on deep learning neural networks. It includes a common feature extraction module using a convolutional neural network (CNN) to extract shallow features from ECG signals; a domain-invariant branch module using a CNN to extract features from the shallow features and perform feature matching for the source domain by minimizing the classification loss, obtaining a prediction; a domain-specific branch module using a CNN with LMMD loss to perform feature matching between each pair of source and target domains for the input shallow features, obtaining n predictions; and a fusion module assigning different confidence levels to each sub-branch and combining the n+1 predictions to obtain the final prediction. The multi-branch network and multi-source unsupervised domain adaptation of this invention effectively learn the general features of ECG signals, enabling the knowledge learned from the source dataset to be applied to the unknown target dataset. Furthermore, by using a prior classifier-based fusion strategy, multiple predictions are organically combined to obtain the final result, further improving the model's generalization performance.

The present invention provides a multi-standard data rights confirmation method based on multi-classifier fusion, comprising: determining the rights confirmation classification standard according to the "three-part principle" and determining the data attribute description of the data to be confirmed; representing the data attribute description in the form of a vector, segmenting and integrating the vector according to the rights confirmation classification standard, and using it as the input vector of the rights confirmation classification model; constructing a rights confirmation classification model including a two-level SVM classifier, and using the primary SVM classifier to classify the power subject, competitiveness, and exclusivity of the data; integrating the classification information obtained by the primary SVM classifier and inputting it into the secondary SVM classifier to obtain the data rights confirmation result. The present invention overcomes the problem of insufficient hypothesis space expression capability existing in machine learning algorithms by constructing a data attribute description of the rights confirmation data and then using the rights confirmation classification model and multi-classifier fusion technology to confirm the data, thereby improving the accuracy of classification and ensuring the security and value of the data.

The invention discloses a wind turbine generator equipment fault diagnosis method and system based on twin multi-classifier fusion, and the method comprises the steps: firstly determining an input sample vector of wind turbine generator equipment fault diagnosis and a corresponding fault type, and carrying out the screening and label reconstruction of wind turbine generator equipment data; and obtaining a twin data set corresponding to the single classifier. Secondly, on the basis of the twinborn data set, constructing twinborn classifiers, and optimizing parameters of each twinborn classifier; then, fault feature samples are collected on line and input into the optimized twin classifier, and reliability distribution of the fault feature samples under all fault categories is obtained; and finally, constructing a twin multi-classifier fusion model based on an evidence reasoning rule, and fusing the reliability output by different twin classifiers to realize the judgment of the fault category of the wind turbine generator equipment. According to the method, the uncertainty of the decision boundary sample is effectively represented, and the diagnosis capability of the classifier on the decision boundary sample of wind turbine generator equipment is improved.

The present invention discloses an adaptive brain region EEG artifact detection method with multi-classifier fusion. The present invention comprises the following steps: 1. filtering multi-channel EEG signals and classifying them into multiple categories of artifacts; 2. analyzing the brain regions and channel correlations corresponding to each category of artifact signals, and obtaining the regional information and channel information of each category of artifacts; 3. extracting features from the channel information of each category of artifacts; 4. performing two-stage feature selection using the ReliefF algorithm and the mRMR algorithm for the regional information and the extracted features, and establishing feature grouping; 5. training a classification model using a machine learning algorithm combined with the selected features; 6. systematically building a plurality of classifiers trained in step 5. The present invention overcomes the tediousness of manual artifact positioning in clinical practice, improves the rapid positioning of abnormal signals, solves the monotony of existing artifact recognition technologies, and can simultaneously realize real-time artifact detection of multi-channel EEG data.

This invention discloses a method for ultra-narrow pulse radar target recognition using prior knowledge-assisted multi-classifier fusion, belonging to the field of radar target recognition technology. This method introduces prior conceptual information about the target into the automated construction process of a tree-like hierarchical structure, compensating for the knowledge deficiencies of data-based automated construction methods. The difficulty of distinguishing between classification nodes in the constructed tree structure is significantly reduced. Therefore, even in recognition scenarios with numerous categories and insufficient data, this invention's method still exhibits good performance in refined target recognition.

The invention discloses a field specific code completion method based on model collaborative reasoning, and belongs to the field of code completion. The method comprises the steps that firstly, a model with a small scale is finely tuned to learn domain specific code knowledge, then the finely tuned model and a large language model are used for completing a code completion task, and feature information output by the model in the reasoning process is collected to be used for training a specific domain-oriented classifier; finally, the classifier is used for fusing reasoning results of different models in the reasoning process to achieve more accurate code completion, specific code knowledge in the field is learned through fine tuning of the language model with the small scale, and compared with direct fine tuning of a large-scale language model, the model deployment and training cost is effectively reduced; and the classifier is trained according to the output extraction features of the large language model and the fine-tuned language model, so that the reasoning results of the large language model and the fine-tuned language model can be adaptively and efficiently combined, and the reasoning precision and speed can be improved.

The invention provides a mobile malicious software detection method based on heterogeneous flow fusion, realizes more effective capture of malicious behaviors in combination with semantic information of heterogeneous flows, and belongs to the technical field of network security. The method comprises the following steps: firstly, explicitly modeling a relationship between entities from different flows by adopting a heterogeneous information network (HIN), and retaining semantic relevance of heterogeneous flows; secondly, a meta-path group is constructed for each stream view, each group comprises content-oriented meta-paths and action-oriented meta-paths, and semantic correlation between applications is established; then, the flow2vec distinguishes HIN entity semantics of different streams based on context constraints; and finally, fusing semantic embedding of each view through a DNN classifier based on channel attention, and weighting contribution of the semantic embedding to realize accurate detection. According to the method, semantics of multiple stream types are integrated, the complementary advantages of the semantics are expected to be utilized, understanding of application program behaviors is enhanced, and malicious information hidden in application program codes is found.

The application provides a downhole drilling working condition recognition method and device based on data driving. The method comprises the following steps: before downhole drilling, the importance of different working condition characteristics is quantified by introducing contribution value, and the output of the working condition classifier of each working condition is subjected to explainability analysis; according to the contribution value of different working condition characteristics to each working condition, a judgment matrix of each working condition is constructed, thereby obtaining a weight vector of each working condition, and an objective weight distribution method driven by data is established; during downhole drilling, downhole measurement while drilling data is collected, and downhole drilling parameter data can be directly obtained; according to the weight vector of each working condition and the working condition index value of each working condition, the working condition of each window is decided through multi-classifier fusion; when a preset number of different working conditions is cumulatively output, the feature vector of each window and the working condition index threshold of each working condition are updated, and a double dynamic updating strategy is established to adapt to the complex and changeable drilling environment. The method provided by the application improves the accuracy and real-time performance of working condition recognition.

According to the credibility modeling method for the big data intelligent processing neural network, the credibility model is established for the identification result of the artificial neural network by using the artificial neuron output layer, so that the credibility model can describe the correct probability of the classification result, that is, the correctness of the classification result is considered through the credibility, and the accuracy of the classification result is improved. The probability degree trust the classification is obtained. Mathematical modeling is carried out by using a neuron output layer, so that a neural network credibility model is successfully established and is used for evaluating a single recognition result of a classifier. On the basis, a traditional method is further improved, a multi-output neural network, a single-classifier rejection algorithm of the credibility of a single-output neural network and a multi-classifier fusion algorithm of the multi-output neural network are established for neural networks of different output types, the credibility evaluation capability of the neural networks is high, the credibility calculation accuracy is high, and the reliability of the neural networks is improved. The neural network is good in reliability, application safety and effectiveness.