29 results about "Class separability" patented technology

The invention discloses a rolling bearing fault classification method fusing adaptive distribution perception discrimination loss (ADADL), and belongs to the technical field of rolling bearing fault diagnosis. The rolling bearing fault classification method comprises the following steps of: obtaining a rolling bearing fault, and carrying out classification on the rolling bearing fault by using the ADADL as a fusion model, and carrying out classification on the rolling bearing fault by using the ADADL as a fusion model, and carrying out classification on the rolling bearing fault by using the ADADL as a fusion model. In a complex industrial environment, classification boundary fuzziness is often caused by noise interference and feature overlapping, and the accuracy of rolling bearing fault diagnosis is reduced. According to the method, an adaptive distribution perception discrimination loss function (ADADL) is provided, and intra-class compactness and inter-class separability are improved by adjusting intra-class distance through a dynamic threshold value and optimizing inter-class distribution through an adaptive boundary. And the cross entropy loss is combined with ADADL, so that the classification precision is further optimized, the model is helped to better process samples difficult to classify, and the robustness and the adaptive ability of the model are improved. The classification performance is remarkably improved on the CWRU data set, and particularly, excellent robustness and generalization ability are shown under the conditions of class imbalance and strong noise. Feature visualization results show that ADADL can optimize clustering boundaries of different fault categories, minimize overlapping regions, and relieve the problem of fuzzy classification boundaries.

The invention relates to the technical field of agricultural remote sensing and cultivated land monitoring, in particular to an accurate cultivated land identification method integrating multi-source remote sensing and phenological response, which comprises the following steps: S100, data acquisition: acquiring time sequence data of multi-source remote sensing in a research area; s200, the phenological period of the crops is extracted; s300, generating a training sample: based on phenological information, obtaining a positive sample by taking an adaptive flowering index as a threshold value; generating a negative sample by adopting a reverse rule of the positive sample rule, and purifying to generate a sample group; s400, feature fusion and optimization: evaluating the category separability of different time phase feature sets by adopting a J-M distance method; s500, classifier evidence fusion: using a D-S synthesis rule to realize classification result fusion; and S600, applications are migrated annually. According to the method, the multi-source remote sensing data are fused, the sample automatic generation strategy is constructed in combination with the phenological period characteristics of the crops, the evidence theory is introduced to fuse the results of the multiple classifiers, and high-resolution and high-stability accurate recognition of the cultivated land in the complex terrain and cloudy and rainy environment is achieved.

The invention discloses a prompt-driven two-stage multi-modal emotion representation learning method. The method comprises the following steps: respectively collecting original video data from a plurality of public multi-modal emotion analysis data sets; preprocessing and feature extraction are carried out to obtain vectorized multi-modal feature representations of vision, audio and texts, and multi-source emotion clues are obtained; in a training stage, a prompt-driven two-stage multi-modal emotion representation learning model is constructed, inter-class separability is enhanced and intra-class intensity features are reserved through an emotion anchor point comparison and alignment stage, including prompt-based emotion anchor point learning and comparison and alignment between joint representation and emotion anchor points; capturing the dynamic change of emotion expression through an emotion intensity offset estimation stage; in the reasoning stage, emotion category prediction and final emotion state prediction are carried out. According to the method, shared emotion features among multiple modes can be stably captured, interference caused by individual differences is effectively suppressed, and the accuracy and robustness of emotion prediction are remarkably improved.

The invention discloses a heterogeneous federated learning method based on attention guidance aggregation and prototype enhancement, and relates to a heterogeneous federated learning method. The invention aims to solve the problem that the existing heterogeneous federal learning method cannot improve the granularity of client feature representation learning and cannot solve class imbalance in global head training. The method comprises the following steps: step 1, constructing a feature aggregation mechanism based on attention guidance; 2, designing a contrast learning objective function, and explicitly enhancing inter-class separability and intra-class compactness of feature representation in a local training process; 3, designing a self-adaptive prototype enhancement strategy, and relieving a class imbalance problem; and 4, constructing a collaborative optimization framework of the global model and the local model. The invention belongs to the technical field of distributed collaborative learning.

This invention discloses a multimodal hash retrieval method, system, device, and medium based on a multi-viewpoint central structure, belonging to the fields of artificial intelligence and multimodal hash retrieval technology. The technical problem this invention aims to solve is achieving a balance between intra-class compactness and inter-class separability during multimodal hash retrieval. The technical solution adopted is as follows: constructing a multimodal dataset; constructing a multimodal hash retrieval model based on a multi-viewpoint central structure; and training the model. Specifically, constructing the multimodal hash retrieval model based on a multi-viewpoint central structure involves: modality-specific prototype learning: using an image modality deep multilayer perceptron and a text modality deep multilayer perceptron to extract refined features from the corresponding modalities, and calculating the average value of the refined features of the image modality and the text modality to obtain modality-specific prototypes, thereby obtaining unique features of the image modality and unique features of the text modality; multimodal ensemble class prototype learning; and multi-view semantic enhancement hash learning.

The application discloses an iris recognition method and device, system and storage medium, and performs preprocessing such as polar coordinate unfolding on an input original iris image in sequence; multi-level image features are extracted through an inverse residual module; subsequently, a global depth convolution GDConv operator is introduced to replace a global average pooling layer, so that spatial structure distribution features of iris textures are retained under the premise of extremely low calculation complexity; finally, an ArcFace loss function is used to optimize the distribution of features in a hyperspherical space, and the class separability of the features is enhanced by introducing an additive angle interval. By using the technical scheme of the application, the technical problems that spatial topological information is lost due to a global average pooling GAP operation in iris feature extraction by using an existing lightweight convolutional neural network, and the feature discrimination is reduced due to network lightweight are solved.

The application discloses a kind of key equipment intelligent diagnosis method and system for rotating machinery, it is related to the technical field of fault diagnosis and prediction, including, with physical inspiration as guidance, in feature learning stage, introduce bounded metric encapsulation mechanism, traditional unbounded feature space is mapped into bounded similarity manifold, to suppress high-dimensional space distance concentration effect and improve inter-class separability and intra-class compactness.In bounded metric space, realize multi-fault identification by combining near neighbor or support vector classifier.The application has significant improvement in weak fault (germination period) detection, composite / multiclass fault distinction and robustness under low signal-to-noise ratio condition, has good explainability and engineering deployment value, is suitable for the predictive maintenance and online health monitoring of wind power, rail transit, aerospace and process manufacturing and other scenes.

The present application relates to remote sensing image processing technical field, specifically a kind of tensor singular spectrum analysis method for hyperspectral image three-dimensional feature extraction, including based on spatial self-similarity adaptive embedding, decomposition and low rank representation based on t-SVD, feature image and classification, compared with prior art, through adaptive embedding and t-SVD process, a new adaptive embedding operation, utilize the spatial similarity feature of HSI, jointly utilize target pixel and non-local similar pixel, combine with re-projection operation, keep target inter-class difference while enhanced intra-class similarity, by designing a trajectory tensor, combined with t-SVD, jointly represent the global low rank feature of HSI, the arrangement of similar pixel in trajectory tensor makes it have low rank feature, and further extract low rank feature by truncated t-SVD, realize the feature extraction of three-dimensional hyperspectral image, and then improve the class separability in hyperspectral image classification.

This invention belongs to the interdisciplinary field of medical image analysis and deep learning, and discloses a method and apparatus for pathological slide image segmentation using multimodal supervised contrastive learning. The method generates biological prior feature maps through decoupling, extracts visual and semantic features by combining the master encoder and prior encoder, and achieves feature alignment using a cross-modal attention mechanism. Supervised contrastive learning is introduced to enhance intra-class compactness and inter-class separability. A multi-scale decoupled decoder is designed, with structural similarity loss and boundary-aware loss respectively supervising structural and detail branches. Finally, dynamic weighted fusion and pseudo-label calibration strategies are used to optimize segmentation performance, significantly improving the accuracy and robustness of pathological image segmentation.

This invention discloses an open-set specific radiation source identification method and system based on multi-granularity embedding guidance. First, selective attention complex convolution branches and Transformer branches are used to extract local fine-grained features and global temporal dependency features of radio frequency signals, respectively. Then, an attention-guided multi-scale feature fusion module performs cross-granularity alignment and semantic fusion on the dual-path features. A label-aware discriminative prototype embedding space is constructed, and joint loss is used to optimize the embedding space to enhance intra-class aggregation and inter-class separability. Unidentified unknown class samples are projected into the discriminative embedding space, and an angle-based prototype incremental update mechanism is used to further identify and subdivide the unknown categories. This invention can simultaneously achieve high-precision identification of known radiation sources and detection and subdivision of unknown radiation sources in open-set scenarios, significantly improving generalization ability and identification stability in complex real-world environments.

The invention provides an open set hyperspectral image accurate classification method based on difficult sample learning and dense spectral spatial feature enhancement, and belongs to the field of hyperspectral image processing. Firstly, a training sample is mapped into a dense spectral feature model, multi-layer information accumulation and fusion are achieved, spectral local changes are modeled finely, and the discrimination between adjacent categories is improved. Secondly, a spectral feature compression model is constructed, known class feature aggregation is promoted, meanwhile, a spatial modeling branch is introduced, and the sensing capacity for complex structures such as edges and textures is enhanced; and a class anchor point strategy is further adopted, so that intra-class polymerization and inter-class separability are improved, and a class center structure is stabilized. For negative samples which are difficult to distinguish, a difficult sample learning strategy is introduced, boundary learning is enhanced, and the unknown class recognition capability is improved. And finally, through the distance between the sample and the anchor point, the ground feature category is obtained, and an effective new thought is provided for open set classification of the hyperspectral image.

A semi-supervised medical image segmentation method based on diffusion-driven hard-soft prototype comparative learning belongs to the field of medical image processing and comprises the following steps: acquiring a medical image and dividing the medical image into a labeled data set and a non-labeled data set; generating a pixel-level confidence map by using a discriminator; using a confidence map to guide a diffusion model to carry out adaptive denoising and correction on the noise pseudo label, and introducing a local detail enhancement mechanism to generate a corrected pseudo label; a hard-soft collaborative prototype comparison module is constructed, a category prototype is constructed based on the corrected pseudo tag, a hard matching indication matrix is generated for high-confidence pixels, soft guidance constraint is applied to low-confidence pixels, and intra-class compactness and inter-class separability of a feature space are enhanced; performing iterative optimization on the model through a joint optimization objective function; inputting the medical image to be segmented into the trained segmentation network to obtain a segmentation result; according to the method, the problems of pseudo label noise accumulation and feature discrimination degradation in semi-supervised learning are solved, and the dependence on large-scale labeled data is reduced.

The invention belongs to the technical field of image recognition, and particularly relates to a CT image motion artifact classification model construction method and system based on feature prototype contrast learning. According to the classification method based on the prototype, the class center is used as feature storage, the inter-class separability and the intra-class consistency in the artifact classification task are enhanced, and the robustness and the classification precision of the model are remarkably improved. The method specifically adopts a Vision Transform (ViT) as a basic model for feature extraction, combines a strong global modeling capability, effectively captures long-range dependence and fine-grained features in artifact detection, and improves the capability of identifying the complexity of artifact types. And by introducing a prototype contrast learning strategy, the feature representation of the artifact image is optimized, and the overfitting problem of the model when training data is insufficient is relieved, so that the generalization ability and robustness of the model in practical application are improved, and an excellent classification result is obtained on a clinical data set.

This invention relates to a method for identifying individual communication radiation sources based on a multi-scale residual prototype learning network. The method comprises a signal preprocessing module, a network training and feature extraction module, a storage module, and an identification module. The signal preprocessing module is connected to the network training and feature extraction module, which is connected to the storage module, and the storage module is connected to the identification module. This invention extracts features from individual communication radiation source signals using a multi-scale residual prototype learning network and then uses a joint decision-making method to identify the attributes of the individual radiation source signals. In this method, MSRPLNet lays the foundation for identifying the features of individual communication radiation source signals by enhancing the inter-class separability and intra-class compactness of the learned features.

The present application belongs to the technical field of computer, and particularly to a face forgery detection system based on uncertain modeling.The system comprises a probabilistic Transformer module, an image block screening module and an uncertainty-aware single classification loss function module.The present application firstly models the dependency relationship between image blocks as Gaussian random variables, extends the Transformer model in a probabilistic manner, then introduces an image block selection module to identify areas with high uncertainty information for final classification, and finally quantifies the uncertainty of the entire image, uses the designed uncertainty-aware single classification loss function to make the model focus more on samples with high uncertainty and difficult to determine, and through only enhancing the internal compactness of real faces, improves the inter-class separability of real and false classes in the embedding space.

The invention discloses an open set specific radiation source identification method and system based on multi-granularity embedding guidance. The method comprises the following steps: firstly, respectively extracting local fine granularity features and global time sequence dependence features of radio frequency signals by using a selective attention complex convolution branch and a Transform branch; carrying out cross-granularity alignment and semantic fusion on the dual-path features through an attention-guided multi-scale feature fusion module; constructing a label perception discrimination prototype embedding space, and optimizing the embedding space by using joint loss to enhance intra-class aggregation and inter-class separability; and projecting the rejected unknown class sample to a discriminative embedding space, and realizing further identification and subdivision of the unknown class by using an angle-based prototype increment updating mechanism. According to the invention, in an open set scene, high-precision identification of a known radiation source and detection and subdivision of an unknown radiation source can be realized at the same time, and generalization ability and identification stability in a complex real environment are significantly improved.

The invention discloses a key equipment intelligent diagnosis method and system oriented to rotating machinery, and relates to the technical field of fault diagnosis and predication, and the method comprises the steps: taking physical inspiration as guidance, introducing a bounded measurement packaging mechanism in a feature learning stage, mapping a conventional unbounded feature space into a bounded similarity manifold, and carrying out the analysis of the bounded similarity manifold; therefore, the distance concentration effect of a high-dimensional space is inhibited, and the inter-class separability and the intra-class compactness are improved. And realizing multi-fault identification in combination with classifiers such as neighbors or support vectors and the like in a bounded metric space. According to the method, the robustness in weak fault (germination stage) detection, composite / multi-class fault distinguishing and low signal-to-noise ratio working conditions is remarkably improved, good interpretability and engineering deployment value are achieved, and the method is suitable for predictive maintenance and online health monitoring of scenes such as wind power, rail transit, aerospace and process manufacturing.

The present application relates to the technical field of remote sensing change detection, and discloses a remote sensing change detection method and system based on change decoupling enhancement, comprising: acquiring double-time-phase remote sensing images, constructing a change decoupling enhancement network model, the model comprising a double-branch encoder, a change representation decoupling module, a semantic consistency enhancement module and a segmentation head, the double-branch encoder extracts double-time-phase features of a double-time-phase image pair at different scales, the change representation decoupling module explicitly identifies and separates unchanged components by calculating the similarity between the double-time-phase features, the semantic consistency enhancement module enhances intra-class consistency and inter-class separability by dividing the change representation into multiple semantic groups and independently refining them, and the segmentation head performs remote sensing change detection according to semantic features. The present application can improve the semantic discrimination ability of change detection, thereby improving the accuracy of remote sensing change detection.

The invention provides a retinopathy detection method fusing multi-focus segmentation and comparative learning, and designs a two-stage network by closely fusing focus segmentation and grading tasks. In the lesion segmentation task of the first stage, an improved MCA-UNet network is provided, the feature extraction capability is optimized by introducing a semi-wavelet collaborative attention module, and high-precision lesion spatial feature support is provided for the subsequent grading task. In the DR grading task of the second stage, a double-branch prototype comparison learning model is constructed, and a feature learning branch strengthens intra-class compactness and inter-class separability by constructing a class prototype, so that the problem of class imbalance ubiquitous in a DR data set is effectively relieved.

The application discloses a deep face recognition method based on a minimum margin loss function, and comprises the following steps: S1, preparing a data set, wherein no face image that may overlap with a reference data set is contained in the data set; S2, using a Tensorflow framework to build a network model based on an Inception-ResNet-v1 model; S3, inputting pictures of a training set in the data set into the network model to perform training; and S4, inputting pictures of a test set in the data set into the trained network model to recognize face images. The application combines the advantages of a Softmax loss function, a center (Center Loss) loss function and a minimum margin (Minimum Margin Loss) loss function, wherein the center loss is used for enhancing in-class compactness, and the Softmax loss and the minimum margin loss are used for improving inter-class separability. Experimental results show that the proposed minimum margin loss makes the technical performance of face recognition reach a new height and reduces the negative influence caused by margin preference.

The present application belongs to the field of fault diagnosis technology of high-speed train transmission system, and discloses a train bogie few sample cross-domain fault diagnosis method based on double path feature reconstruction. In view of the problems of sample scarcity, domain offset under cross-condition, cross-fault type and cross-equipment scene, firstly, one-dimensional vibration signal is converted into two-dimensional time-frequency graph through continuous wavelet transform, and deep features are extracted through convolution feature embedding network; then, a self-attention feature enhancement module is constructed, and fine-grained fault mode is extracted through position coding, self-attention weighting and nonlinear mapping; further, a bidirectional variation correction module is designed, the support set is used to reconstruct the query set to enhance the class separability, and the query set is used to reconstruct the support set to constrain the intra-class compactness; finally, the forward and backward reconstruction distances are fused through a double metric diagnosis module to obtain the fault category. The method can improve the cross-domain few sample fault diagnosis precision and robustness under the condition of very few target domain labeled samples.

The application provides a gas transient signal rapid detection method based on Mamba and ordinal metric learning, and relates to the technical field of gas detection and analysis. In view of the problems that the existing gas sensor relies on steady-state signals, resulting in long detection time, insufficient utilization of transient signals, and high intra-class variance and poor inter-class separability of transient signals, the application proposes a MOML framework based on Mamba encoder and triplet loss metric learning. The framework uses the Mamba encoder to extract long sequence features from the transient dynamic response signals of the gas sensor and map them to the embedding space; the embedding space is optimized through the triplet loss to enhance the intra-class compactness and inter-class separability; especially for the gas concentration regression task, a neighborhood negative sampling strategy is designed to realize ordinal metric learning. The experimental results show that the application can significantly improve the feature separability, improve the gas type recognition accuracy and concentration prediction accuracy, and realize low-delay and high-precision gas detection.

The invention discloses a hyperspectral image ensemble classification method combined with TRP matrixes of different dimensions. A fitness function is designed by adopting a genetic algorithm and considering category separability and low-dimensional correlation at the same time, TRP matrixes of different dimensions are optimized, an optimal TRP matrix set is obtained, and adaptive selection of projection dimensions is realized; secondly, projecting the hyperspectral image to a corresponding low-dimensional space by using the optimized TRP matrixes with different dimensions, and classifying each piece of low-dimensional data by using a support vector machine; fusing the plurality of classification results by adopting a majority voting strategy to obtain a more accurate and stable final classification result; in order to verify the effectiveness of the method, experiments are carried out on real images, and results show that the method is excellent in calculation efficiency and classification precision.

The invention discloses a knowledge enhancement recommendation method based on a large language model and multi-view comparative learning, relates to a data mining and graph topological structure analysis technology, and provides a scheme for solving the problems that an existing method in the prior art is insufficient in representation discrimination, an embedded space is prone to collapse and the like. The method comprises the following steps: preprocessing data, constructing a user-article bigraph, carrying out semantic embedding based on a large language model, constructing multiple views after learning graph neural network collaborative representation, carrying out comparative learning on the multiple views, optimizing the model, calculating total loss, and generating recommendation and outputting the recommendation. The method has the advantages that the representation capability of a recommendation system is remarkably enhanced, the problems of excessive smoothness and sensitivity to noise in the graph convolution process are relieved, discrimination signals in contrast learning are enhanced, good inter-class separability is kept, and recommendation sorting performance is centrally optimized.

The application discloses a kind of semi-supervised industrial surface defect segmentation method, system and storage medium, belong to defect segmentation field, including: training phase and application phase.In training phase, direction consistency regularization based on confidence guide is used instead of average consistency regularization, and the quality of feature alignment is improved;At the same time, the confidence perception hybrid pseudo-label method generation method is designed, and the influence of confirmation bias of unlabeled data is reduced;And, the foreground-background contrast learning strategy is introduced to improve the class separability of feature space, encourage normal area or defect area to have similar representation in feature space, while the features between normal area and defect area are far away from each other, guide the model to identify defect features more accurately;In the case of limited data labels, additional supervision signals can be extracted from a large amount of unlabeled data, the training process of the model is completed, and the prediction accuracy of the model is effectively improved.

The invention provides a gas transient signal rapid detection method based on Mama and ordinal number metric learning, and relates to the technical field of gas detection and analysis. Aiming at the problems of long detection time, insufficient utilization of transient signals, high intra-class variance and poor inter-class separability of the transient signals caused by dependence on steady-state signals of an existing gas sensor, the invention provides an MOML framework based on a Mama encoder and triple loss metric learning. The framework utilizes a Mama encoder to extract long sequence features from transient dynamic response signals of a gas sensor and map the long sequence features to an embedding space; the embedding space is optimized through triple loss, and intra-class compactness and inter-class separability are enhanced; especially for a gas concentration regression task, a neighborhood negative sampling strategy is designed to realize ordinal number metric learning. Experimental results show that the characteristic separability can be remarkably improved, the gas type recognition precision and the concentration prediction accuracy are improved, and low-delay and high-precision gas detection is achieved.

The application discloses a kind of enhanced field adaptation's vibrating screen cross-domain fault diagnosis method, comprising: the different field vibrating screen multidimensional comprehensive fault data set obtained is divided into source domain feature training set, target domain feature training set and target domain feature test set;Source domain feature training set and target domain feature training set are mapped to random feature space and are processed to joint distribution difference, to execute the field and class alignment of feature;Source domain feature training set is processed to field feature discrimination, to enhance the intra-class tightness and inter-class separability of feature;According to the joint distribution difference processing result and the field feature discrimination processing result, based on the enhanced field adaptation incremental random vector function chain network, a vibrating screen cross-domain fault diagnosis model is constructed, which is used to predict the fault class of unknown label in target domain. The application effectively overcomes the challenge of domain shift, and also significantly improves the performance and generalization ability of the vibrating screen fault diagnosis model.

The application discloses a remote sensing target detection method and system based on double-prior expansion and subspace diffusion. After an initial multispectral or hyperspectral remote sensing image is acquired, implicit spatial-spectral feature continuous representation and geometric constraint technology are applied for mathematical modeling, and continuous implicit spatial-spectral features are extracted. Sparse filtering and dynamic adaptive threshold processing are used to purify the features, and target and background noise separation is optimized. Based on the requirement of saliency detection, the feature channel weight is adjusted to enhance the inter-class separability, the weighted saliency feature is generated, the frequency domain separation is performed, the features are decomposed into high-frequency and low-frequency components, the denoising diffusion processing is performed in the low-dimensional abundance map subspace, and the two parts of features are recombined to generate a mask image for remote sensing salient target detection. The method of the application not only solves the limitations of traditional networks in background anti-interference and boundary description, but also overcomes the algorithm power bottleneck caused by high-dimensional remote sensing data, and fills the feature gap between image reconstruction and downstream detection tasks.

The invention relates to the technical field of load feature extraction, and discloses a load feature self-extraction method, system, equipment and medium based on supervised metric learning, and the method comprises the steps: obtaining original current and voltage signals of a target electric appliance, and generating a multi-dimensional initial power fingerprint feature set through time-frequency domain transformation; inputting the feature set into a pre-trained mask auto-encoder, generating a low-dimensional potential feature vector through the encoder, and constructing an intra-class compactness and inter-class separation constraint and joint optimization model by using a supervised metric learning strategy in combination with an electric appliance class label; and finally, outputting a strong-universality load feature vector for non-intrusive load identification. According to the method, self-supervised reconstruction and supervised metric learning are fused, the feature discrimination and generalization ability are effectively improved, dependence on a large amount of labeled data is reduced, and the method is suitable for high-precision equipment identification under complex aliasing signals.