40 results about "Multiple classifier" patented technology

The invention discloses a machine learning enhanced unit commitment problem optimization solving method, which establishes a probability fusion model, effectively combines the advantages of various classifiers, has stronger stability, higher accuracy and better generalization ability compared with a single model, and can more effectively guarantee the solving quality and improve the solving speed. In addition, a variable screening mechanism based on confidence ranking is designed, high-confidence integer variables are fixed preferentially, effective minimum start-stop time constraints are predicted, the variable and constraint scale of the mixed integer programming problem is remarkably reduced, and the solving efficiency is improved. The optimization solution model provided by the invention respectively realizes about 6 times and 11 times of acceleration effects in 96-period and 288-period scenes of the SG-126 system, which shows that the method has good universality, stability and expansibility.

The invention discloses a code risk identification method and system based on a large language model. The method comprises the following steps: submitting a code of a current version on a code management platform, and triggering a submission event; a submission event is captured through Webhook in the code management platform, and an HTTP request is sent to the difference analysis system; analyzing the HTTP request according to a difference analysis system and obtaining a code of a previous version and a code of a current version; comparing the obtained code of the previous version with the code of the current version according to a difference analysis module of the difference analysis system to obtain a difference code of the current version and the previous version and a specific position of the difference code; according to the difference analysis system, the difference codes of the current version and the previous version and the specific positions of the difference codes can be quickly identified, the analysis efficiency is improved, and the accuracy of an analysis result is ensured; and the risk category and the risk level of the difference code can be accurately identified through multiple classifiers of a large language model and a context sensing module.

The present invention provides an industrial equipment fault diagnosis method, system, and storage medium based on multi-view expansion statistical features, which relate to the technical field of industrial equipment fault diagnosis. The method aims to solve the problems that existing methods are easily contaminated by noise, and that the extracted features contain redundant and irrelevant features, resulting in long calculation time, low calculation efficiency, and high hardware computing power requirements. The method comprises the following steps: step 1, obtaining a time series data set of the operating fault status of the industrial equipment; step 2, preprocessing the time series data using an unsupervised differential expansion mapping preprocessor; step 3, constructing multiple filtering feature selectors at different storage ratios to obtain multi-view feature subset vectors and a combined feature selector; step 4, constructing multiple classifiers, training the multiple classifiers based on the multi-view feature subsets, and constructing a combined classifier; and step 5, using the combined feature selector and the combined classifier to perform fault diagnosis on the equipment operating status.

The invention discloses a multi-endpoint reproductive toxicity prediction method and system based on a large language model, and belongs to the technical field of toxicity prediction. The method comprises the following steps: extracting phenotype information of text data in a candidate data set by using the large language model; performing multi-dimensional annotation processing on the phenotype level annotation data set; inputting the reproductive toxicity training set into a pre-constructed molecular representation learning model for training, and constructing a reproductive toxicity prediction model set with phenotype specificity; performing performance evaluation on the plurality of classifiers in the reproductive toxicity prediction model set by using the test data set, and screening according to preset performance indexes to obtain an optimal prediction model combination; performing toxicity prediction on the molecular structure of the chemical substance to be detected by using the optimal prediction model combination to obtain a multi-dimensional reproductive toxicity prediction result including cross-gender, cross-generation and cross-research types; according to the method, high-resolution and multi-dimensional reproductive toxicity phenotype prediction is carried out on the environmental chemical substances.

The invention discloses a feature extraction device, an image matching apparatus and a medium. The feature extraction device comprises a feature extraction module which extracts features with a plurality of scales and a plurality of feature dimensions from an image to be matched; the feature fusion module is used for aligning all or part of the extracted features on the feature dimension, and then aligning the features aligned on the feature dimension in pairs on the scale dimension from small to large and from large to small so as to generate fused features; a classifier module comprising a plurality of classifiers, each classifier being assigned to a corresponding fusion feature and being trained such that a difference between a prediction result of each fusion feature drawn from small to large in a scale dimension and prediction results of all fusion features drawn from large to small is minimum, and vice versa; and the foreground enhancement module divides the fusion features into foreground features and background features according to a preset rule, and adds a class to the background features for another classifier during training.

The invention discloses a surge absorber early failure diagnosis method based on acoustic characteristics. The surge absorber early failure diagnosis method comprises the steps of collecting acoustic signals generated by a surge absorber in an operation state; performing mixed feature extraction by fusing improved variational mode decomposition and frequency cepstrum coefficients, and constructing a high-dimensional initial feature set; pre-screening the high-dimensional feature set by using a minimum redundancy and maximum correlation algorithm, and then performing feature fine optimization by using a support vector machine package method optimized by a quantum behavior particle swarm algorithm to obtain an optimal feature subset; and finally, constructing a plurality of classifier groups based on self-service sampling, and performing decision-making layer fusion on the output of each classifier by using a D-S evidence theory to realize accurate diagnosis of the early failure state of the surge absorber. Through deep fusion of multiple levels and multiple algorithms, the sensitivity, robustness and accuracy of diagnosis are remarkably improved, and non-intrusive online monitoring and early warning of the surge absorber can be realized.

This invention discloses a small-sample fault diagnosis method based on multi-scale ensemble LightGBM, comprising the following steps: acquiring vibration acceleration signals of rotating machinery and extracting samples to obtain training and test sets; constructing a multi-scale ensemble model consisting of a data augmentation module, a label classification module, and an output weighting module; in the data augmentation module, resampling the original samples using a multi-scale sliding window to obtain multiple sub-sample training and sub-sample test sets; in the training phase, training multiple classifiers in the label classification module using the sub-sample training sets; in the testing phase, inputting the sub-sample test sets into the corresponding classifiers, and obtaining the test results of the label classification module on the original samples through ensemble; calculating the frequency domain similarity between the test set and the training set samples using a metric function, and using it as a weight; in the output weighting module, weighting the ensembled test results to obtain the final classification diagnosis result.

The invention discloses a full-color bird target recognition method and system. According to the method, a feature map is extracted from a full-color bird image through a convolutional neural network by using an SSD model, and a priori frame is set to determine the initial position and category information of a bird target. And target tracking is realized by means of a median flow tracking algorithm in combination with a failure detection mechanism and a forward-backward tracking algorithm. A cascade classifier comprising multiple classifiers is used to generate positive and negative samples, a P-N learning algorithm is used to iteratively train the classifiers, and then bird target position and category information is optimized. The problems of low recognition accuracy, insufficient tracking capability and backward data processing and model optimization means in the prior art are solved, accurate recognition and tracking of full-color bird targets are achieved, the bird repelling effect is improved, and safety and stability in related fields are guaranteed.

The present application belongs to the technical field of deep-sea mineral resource exploration, and discloses a deep-sea manganese nodule coverage acoustic characterization method based on multi-classifier decision fusion. By extracting 14 backscattering texture features and 4 seabed topography features, and using Boruta algorithm to optimize the extracted features, a refined feature set for characterizing the seabed is constructed. An iterative robust estimation algorithm based on a sliding window is introduced to identify and suppress gross errors in the feature image by dynamically adjusting the observation weight through continuous smoothing of the data in different directions. In the model construction stage, the Stacking mechanism in ensemble learning is used to integrate the advantages of multiple classifiers for decision-level integration and generate a spatial distribution map of nodule coverage. The Boruta feature selection algorithm is introduced to screen out the key features with the strongest discrimination ability for nodule coverage from the original acoustic features, avoiding redundant interference and improving the accuracy and prediction discrimination ability of the model.

This invention discloses a method and system for identifying the spatial distribution of accumulated water based on multi-classifier collaboration. The method includes: constructing a hierarchical classifier library comprising a preliminary screening layer, an arbitration layer, a re-identification layer, and a verification layer, with each layer containing multiple classifiers of different types. First, the preliminary screening layer performs coarse classification on UAV images, and the results of each classifier are fused using a logical OR operation to determine suspicious areas. Then, the arbitration layer and the re-identification layer sequentially perform refined identification of ambiguous areas, using a logical AND operation to obtain high-confidence accumulated water areas. For the remaining ambiguous areas, the verification layer is activated for final discrimination. The high-confidence accumulated water areas identified by each layer are merged to form a preliminary accumulated water distribution map. Pixel confidence is optimized through weighted fusion, and low-confidence pixels are removed. Finally, post-processing is performed to output the final spatial distribution result of the accumulated water. This application can effectively improve the identification accuracy and robustness of small and irregular accumulated water areas in complex urban scenes.

The present invention discloses a method for constructing a functional connectivity network, a system and an electronic device, including: using a variational autoencoder network to establish a multivariate Gaussian distribution probability model for each brain region of interest through unsupervised learning, using Jensen-Shannon divergence to calculate the degree of similarity between the probability distributions of any two brain regions, and obtaining an adjacency matrix of paired brain regions as a brain functional network; using a two-sample t-test to select a feature set with a higher discrimination, and inputting the selected feature set into a support vector machine for classification; inputting the feature sets of different dimensional models into a support vector machine classifier, and using adaboost enhancement method to combine multiple classifiers to obtain a stronger classifier. The present invention uses a generative learning method to improve the statistical power of group data and the generalization ability of observation data variability in the diagnosis of mild cognitive impairment, and further improves the diagnostic accuracy of mild cognitive impairment through the joint enhancement of multidimensional models.

This invention discloses a classification method for expanding training samples based on multi-classifier recognition results, comprising: selecting multiple classification methods; selecting initial training samples including each category from the dataset to be classified to form an initial training sample set; classifying and recognizing the dataset data using each classification method and the training sample set to obtain the classification results of the dataset to be classified by each classification method; calculating the classification result acceptance rate for each data based on the classification results of the dataset to be classified; comparing the classification result acceptance rate with its preset threshold to obtain new training samples and expand the training sample set; determining and executing the next iteration classification based on the expanded training sample set; and taking the maximum acceptance classification result of the last iteration as the final data classification result of the dataset to be classified. This invention improves classification accuracy by iteratively verifying the results of multiple classifiers and gradually expanding the training samples.

The invention discloses an underwater sound source target identification method based on a dynamic selection integration technology, and the method comprises the steps: obtaining a sound source signal in real time through an underwater sensor, including acoustic features of a submarine target and a sound bait, extracting a time-frequency feature, a modulation feature and a statistical feature through short-time Fourier transform, and fusing the features through PCA (principal component analysis). The method comprises the following steps: generating a multi-dimensional feature vector, establishing a plurality of classifier models, evaluating the performance through cross validation, intelligently selecting an optimal classifier combination, inputting the fused feature vector into the optimal combination for target classification, outputting an identification result, and finally, continuously updating the classifier models by adopting an incremental learning method according to the identification result and an actual situation. And continuous optimization management of underwater sound source target identification is realized. Through a multi-dimensional feature fusion mechanism, time-frequency features, modulation features, statistical features and other information are comprehensively considered, the distinguishing capacity of the model for a target and an interferent is improved, in addition, through intelligent selection of an optimal combination in multiple classifiers, the distinguishing capacity of a submarine target and a sound bait is improved, and the recognition precision is effectively enhanced.

The invention discloses a cross-user wearable activity identification method based on group specific concept perception representation learning. The method comprises the steps of collecting and preprocessing multi-user sensor data; the concept offset degree is measured from a time sequence view angle and a semantic view angle, user clustering is carried out by fusing multi-view-angle measurement results, and group specific concept tags are generated; constructing a perception model comprising an activity encoder, a user encoder and a plurality of classifiers, and performing supervised learning by minimizing the joint loss of activity, user and group specific concept classification; introducing a condition discriminator to construct a representation pair of joint distribution and edge distribution, and minimizing condition mutual information of activity representation and user representation under a group specific concept through adversarial training to obtain a trained model; during application, test data are input into the trained model for activity identification. According to the method, through explicit modeling of specific concepts and decoupling of activity and user features, cross-user concept offset is eliminated, and the activity identification generalization ability of the model on new users is significantly improved.

This invention relates to a method, device, and storage medium for traffic mode classification based on crowdsourced navigation trajectories. The method includes: S1, extracting motion features and geographical features of sample trajectories based on sample trajectory data and geographic information data; S2, training and testing multiple classifiers based on the motion features and geographical features extracted in S1, and selecting the classifier with the highest classification accuracy as the best classifier through accuracy evaluation; S3, segmenting the collected raw navigation trajectory data based on change points to obtain segmented navigation trajectory data; S4, calculating the motion features and geographical features of the navigation trajectory data based on the segmented navigation trajectory data and geographic information data; S5, inputting the segmented navigation trajectory data into the best classifier trained in S2 based on the motion features and geographical features of the navigation trajectory data obtained in S4, and predicting the traffic mode category of the navigation trajectory. Compared with the prior art, this invention has the advantage of high recognition accuracy.

The application discloses a strip plate shape intelligent regulation and control method based on an adaptive fusion model, which comprises the following steps: step 1, collecting historical rolling production process data and performing pretreatment; step 2, constructing a rolling production process data set based on the pretreated data; step 3, classifying plate shape quality according to the ratio of strip crown and target thickness and setting a category label; step 4, establishing an adaptive strip outlet plate shape diagnosis model containing multiple classifiers based on a DS theory, and training the diagnosis model through the rolling production process data set; step 5, inputting rolling production process data under a new rolling schedule into the classifier of the trained adaptive strip outlet plate shape diagnosis model to obtain a real-time classification prediction result of the strip, and if the prediction classification is under-crown or over-crown, step 6 is executed; otherwise, the process parameters are not adjusted; and step 6, dynamically adjusting and optimizing the process parameters based on an inverse linear quadratic control (ILQ) according to the prediction result of step 5.

The invention is suitable for the technical field of proteomics, and provides a generative adversarial network-based protein post-translational modification data enhancement method. According to the method, class imbalance can be effectively relieved, the positive sample recognition capability can be enhanced, minority class pseudo samples are generated through an improved clustering enhancement conditional generative adversarial network (RP-CGAN), features are extracted in combination with an ESM-2 pre-training protein language model, and the positive sample recognition capability and generalization capability are improved; eSM-2 feature extraction and RP-CGAN data enhancement technologies are fused, so that the overall prediction performance and classification stability are improved, and key indexes of multiple classifiers are remarkably improved; through multi-dimensional index screening and self-adaptive regulation and control, it is ensured that enhanced data is close to real distribution; by means of a lightweight architecture and a convergence strategy, the training efficiency and the model stability are optimized; the method has strong stability and cross-domain potential, can be migrated to other protein modification prediction tasks, and has application potential in cross-domain unbalanced classification scenes.

This invention is applicable to the field of proteomics and provides a method for enhancing protein post-translational modification data based on generative adversarial networks (GANs). This method can effectively alleviate class imbalance and enhance the ability to identify positive samples. It generates minority class pseudo samples through an improved cluster-enhanced conditional GAN ​​(RP-CGAN), and extracts features using the ESM-2 pre-trained protein language model to improve both positive sample identification and generalization capabilities. The method integrates ESM-2 feature extraction with RP-CGAN data enhancement technology to enhance overall prediction performance and classification stability, significantly improving key metrics for multiple classifiers. Multi-dimensional indicator screening and adaptive regulation ensure that the enhanced data approximates the true distribution. Leveraging a lightweight architecture and convergence strategy, the method optimizes training efficiency and model stability. The method possesses strong stability and cross-domain potential, enabling migration to other protein modification prediction tasks and possessing potential for application in cross-domain imbalanced classification scenarios.

The invention provides a Chinese pre-training sample quality evaluation method and device based on multiple classifiers. The method comprises the following steps: determining a Chinese pre-training sample to be subjected to quality evaluation; obtaining a plurality of quality evaluation results of the Chinese pre-training sample based on a plurality of pre-trained classifiers; wherein the plurality of classifiers are heterogeneous classifiers; and fusing the plurality of quality evaluation results to obtain a target quality evaluation result of the Chinese pre-training sample. According to the method, the quality of the Chinese pre-training sample is evaluated through the plurality of pre-trained heterogeneous classifiers, so that the prejudice of a single model can be avoided, the stability and the reliability of the overall quality evaluation are improved, the training requirements of a large-scale language model are effectively supported, and a new technical path is provided for the quality evaluation of the Chinese pre-training data.

This specification discloses a method and apparatus for predicting turning intentions. An unmanned driving device acquires an image of an obstacle and extracts a directional gradient histogram of the obstacle's boundary. Based on the directional gradient histogram and a trained first classifier, the device determines the obstacle's first turning intention. The device then uses multiple trained second classifiers to determine the obstacle's second turning intentions in each possible direction of travel. Finally, based on the first and second turning intentions, the device determines the obstacle's final turning intention, thereby determining an obstacle avoidance strategy based on the final turning intention. By predicting the obstacle's turning intention based on the directional gradient histogram of the obstacle's boundary, computational complexity is reduced, prediction timeliness is improved, and prediction accuracy is balanced by using multiple classifiers to determine the final turning intention.

The application discloses a cross-user wearable activity recognition method based on group-specific concept-aware representation learning, which comprises the following steps: collecting multi-user sensor data and preprocessing; measuring the concept drift degree from the time sequence perspective and the semantic perspective respectively, fusing the multi-perspective measurement results to perform user clustering, and generating group-specific concept labels; constructing a perception model comprising an activity encoder, a user encoder and multiple classifiers, and performing supervised learning by minimizing the joint loss of activity, user and group-specific concept classification; introducing a conditional discriminator to construct a representation pair of joint distribution and marginal distribution, minimizing the conditional mutual information of activity representation and user representation under the group-specific concept through adversarial training, and obtaining a trained model; and inputting test data into the trained model for activity recognition. The application explicitly models the group-specific concept and decouples the activity and user features, eliminates the cross-user concept drift, and significantly improves the activity recognition generalization ability of the model on new users.

According to one aspect, a method for distributed sound / image recognition using a wearable device includes receiving sensor data via at least one sensor device and detecting, by a classifier on the wearable device, whether the sensor data includes an object of interest. The classifier is configured to execute a first machine learning (ML) model. The method includes, in response to detecting the object of interest within the sensor data, transmitting the sensor data to a computing device via a wireless connection, wherein the sensor data is configured to be used by a second ML model on the computing device or a server computer for further sound / image classification.

The invention discloses a cross-domain target detection method based on characteristic decomposition, a computer program product, a storage medium and a terminal, and belongs to the technical field of target detection. The method comprises the following steps: generating an intermediate domain image which retains high-level semantic features of a source domain and a target domain through an intermediate domain generator; fusing the last three layers of features of the backbone network through a multi-scale feature fusion module; a feature decomposition module is introduced, the module comprises a global decoupling module and an instance-level decoupling module, and consistent information representation between a source domain and a target domain is achieved through feature decoupling and utilization; and setting a subtask alignment module which comprises a plurality of classifiers and locators as auxiliary predictors and is used for adjusting the feature representation of the intermediate domain and the target domain and realizing the decoupling optimization of classification and positioning tasks. According to the method, the problems of insufficient context information utilization, feature representation coupling and the like in the prior art are effectively solved, the performance in multiple cross-domain scenes is improved, and the average precision is improved by about 1.5%.

A machine learning device includes: a processor that processes data samples; and a storage device that stores processing results. The processor generates multiple classifiers based on multiple learning databases. The multiple learning databases each store multiple learning data samples. The processor generates evaluation results of the recognition performance of each of the multiple classifiers and, based on the evaluation results, determines one of the multiple learning databases and a classifier generated based on the one learning database as the learning database and classifier to be used.

The application provides a citation network node classification method and system fusing GCN and multiple classifiers, wherein the classification method comprises the following steps: regarding the whole citation network as a non-directional graph G comprising node attributes and topological structures; calculating an adjacency matrix and a feature matrix of the non-directional graph G; initializing an edge weight matrix of the non-directional graph G, and calculating the edge weight of the non-directional graph G according to the feature matrix; converging the calculated edge weight to the nodes at both ends of the edge, updating the feature matrix; filtering and amplifying the features in the updated feature matrix; inputting the adjacency matrix and the updated feature matrix into n classifiers respectively for prediction and fusing the prediction results to obtain the final classification result. The classification method comprehensively considers the effective semantic information in the network, can obtain more accurate and stable classification effect through the prediction of multiple classifiers and the fusion by using fuzzy integration.

The invention discloses an unbalanced sample-oriented alarm flooding data enhancement and root diagnosis method, which comprises the following steps of: acquiring alarm event data generated in an industrial system; the improved SMOTENC algorithm performs data enhancement on minority class samples, and a synthetic alarm sample having highly similar sequential logic with an original fault sample is generated in a feature space to balance a data set; constructing a group of hidden Markov model variant classifiers with different structures; evaluating the performance of each variant classifier by adopting different indexes; and taking different performance indexes as references, fusing diagnosis results of a plurality of classifiers by adopting an integrated learning framework of a weighted voting method, and finally outputting an alarm source diagnosis result with high confidence in real time. By improving the data enhancement method, the problem of sample imbalance in the industrial alarm data can be effectively solved; the advantages of different HMM variants are integrated through an innovative model integration framework, and the accuracy of alarm flooding root diagnosis can be remarkably improved.

The present invention provides a method for human posture recognition, comprising: acquiring posture data uploaded by a wearable device; performing preset classification on the posture data to obtain non-periodic posture data and periodic posture data; decomposing the non-periodic posture data to obtain a first time series feature matrix; performing spatial feature extraction on the periodic posture data to obtain a spatial feature matrix, performing signal decomposition on the periodic posture data to obtain a second time series feature matrix, and fusing the spatial feature matrix with the second time series feature matrix to obtain a fused feature matrix; classifying the time series feature matrix to obtain a first posture feature; inputting the fused feature matrix into multiple classifiers for classification respectively to obtain multiple classification results, and voting on the multiple classification results to obtain a second posture feature; performing judgment based on the first posture feature and the second posture feature to obtain a posture recognition result; and improving the accuracy of posture recognition.

Multiple classifier models are applied to features of a circuit design after processing the design through a first phase of an implementation flow. Each classifier model is associated with one of multiple directives, the directives are associated with a second phase of the implementation flow, and each classifier model returns a value indicative of likelihood of improving a quality metric. Regressor models of each set of a plurality of sets of regressor models are applied to the features. Each directive is associated with one of the sets of regressor models, and a combined score from each set of regressor models indicates a likelihood of satisfying a constraint. The directives are ranked based on the values indicated by the classifier models and scores from the sets of regressor models, and the circuit design is processed n the second phase of the implementation flow by the design tool using the directive having the highest rank.

The invention provides a remote sensing image scene classification method and device based on a multi-stream self-distillation architecture. The multi-stream self-distillation network architecture composed of a main stream and two branch streams is constructed. The main stream is constructed based on a CNN model, and the two branch streams are constructed based on a MobileNetV2 model. Attention modules are introduced into the two branch streams to enhance feature interaction with the main stream. In addition, a feature fusion strategy and a classification result integration strategy are introduced into the architecture to construct a feature fusion classifier and an integrated classifier. During architecture training, a self-distillation strategy is adopted, and an integrated classifier is used as a teacher model to guide training of each branch model, a trunk model and a feature fusion classifier. Experiments on four remote sensing image data sets show that the multi-stream self-distillation architecture can improve the classification precision on the basis of not changing the CNN network structure, and output multiple classifiers with different precision and resource consumption levels to adapt to multiple practical application scenes such as edge calculation and high-precision recognition.