7results about How to "Solve overfitting" patented technology

The invention discloses a sparse time sequence Bayesian network construction method and system based on power distribution network topological constraints, and the method specifically comprises the steps: constructing a power distribution network topological graph, and calculating an adjacent matrix between nodes and a k-hop neighborhood matrix Nk; based on the power distribution network topology distance information and the matrix Nk, generating a hard constraint rule, and constructing a topology dependence mask matrix M; calculating an electrical influence range of the fault point on surrounding nodes to obtain an electrical influence matrix E; if the electrical influence coefficient of one node on the other node is smaller than a set value, deleting the corresponding dependent edge; introducing a data source reliability matrix R, and performing hard deletion or soft weakening on edges with reliability lower than a threshold value; combining the matrixes M, E and R to synthesize a sparse structure matrix S; and taking the matrix S as a space skeleton, adding a time dimension autoregression edge, and constructing a complete sparse time sequence Bayesian network. According to the invention, by guiding the rarefaction of the network structure, the number of network edges and the number of parameters are effectively reduced, and the trainability and reasoning efficiency of the model are improved.

The application discloses a subject-driven personalized generation method based on decoupled mask prompt attention fine-tuning, and belongs to the field of deep learning, computer vision and artificial intelligence generated content. A mask prompt decoupling module is designed to decompose the unified text prompt into a text prompt containing only the subject and a text prompt containing only the context. A subject attention focusing module and a context attention adjusting module are introduced, and independent subject feature extraction paths and context semantic adaptation paths are established, respectively. The subject identity learning and the context scene modeling are explicitly separated, and the double constraint loss guided by the mask is used for joint optimization to further prevent feature coupling. Finally, the algorithm effectively preserves the fine appearance features of the subject, significantly enhances the adaptability of the model to novel context instructions, and effectively improves the quality of personalized generation and the flexibility of context editing.

The invention discloses a complex scene traffic perception method and system based on fuzzy logic and data enhancement, and relates to the technical field of automatic driving environment perception. The method comprises the steps of obtaining an RGB image and point cloud data, and performing preprocessing operation to obtain a sample set; expanding the sample set by using a forced enhancement mechanism to obtain an expanded sample set; performing exclusive feature enhancement operation on the expanded sample set according to the scene complexity to obtain enhanced features; performing dynamic weight calculation and feature fusion on the enhanced features based on factors influencing traffic perception by using a fuzzy logic algorithm to obtain fusion features; and performing perceptual prediction on the fused features by using a perceptual prediction model to obtain a perceptual prediction result. The method combines forced weather enhancement and fuzzy logic fusion technologies, adapts to complex scenes, and solves the problem that multi-modal sensing robustness and generalization are insufficient in severe weather.

This disclosure relates to the field of face recognition technology, and provides a training method and apparatus for a face recognition model that avoids the long tail problem of data. The method includes: constructing a face recognition model; obtaining a training dataset, and executing the following loop to train the face recognition model in multiple rounds: sampling the current round of training from the training dataset using a dynamic sampler to obtain a sample set used for the current round of training; inputting the sample set into a feature extraction network to obtain a feature vector set corresponding to the sample set; inputting the feature vector set into a normalization network to normalize the feature vectors in the feature vector set; calculating a classification loss using a classification network and a contrastive loss using a contrastive network based on the feature vector set processed by the normalization network; updating the model parameters of the face recognition model based on the classification loss and contrastive loss; incrementing the training round number corresponding to the current round of training by one; and ending the loop when the training round number equals a preset round number.

The invention discloses an analysis method and system for large-scale multi-type customer order data, and the method comprises the following steps: extracting a remark demand text of the order data, carrying out the data preprocessing, extracting a hidden layer feature vector, decomposing the hidden layer feature vector into a plurality of business semantic features, outputting a decoupling enhancement feature, and carrying out the data preprocessing; calculating the similarity between the intention semantic feature and a preset intention prototype, and outputting an intention activation distribution vector; calculating a matching confidence coefficient for each knowledge domain, and generating a knowledge feature vector; separating a corresponding semantic subspace feature from the decoupling enhancement feature, calculating a gating coefficient with an intention activation distribution vector, performing element-by-element product operation on the gating coefficient and a knowledge feature vector to obtain knowledge representation, and splicing the knowledge representation and the decoupling enhancement feature to obtain a fused semantic feature; and performing intention recognition and entity extraction based on the fused semantic features, and outputting intention probability distribution and a service entity sequence. According to the invention, the automation degree and reliability of customer special demand analysis can be improved.

The application discloses a medical document image classification method based on multi-modal fusion, relates to the technical field of medical information processing and artificial intelligence, and realizes automatic recognition and classification of nine types of documents; single-label annotation and consistency review of medical document images are performed based on annotation software; standardization preprocessing is performed on the images, an OCR engine is called to extract text to form an image mode and a text mode; a document image Transformer classification model and a Chinese pre-training language model classifier are trained respectively; under the same hierarchical cross-validation division, two types of features are extracted and late fusion training is performed to obtain a fusion classifier; single, batch and directory level inputs are supported in the reasoning stage, and the output category and confidence are output, and difficult samples can be located through cross-modal divergence analysis to guide data iteration; the application has higher robustness and explainability under long-tail category distribution, and is suitable for medical document automatic archiving, quality control auditing and data governance and the like.

The application discloses a smart contract vulnerability detection method based on multi-view learning, and the method obtains three representation modes of a smart contract source code, an abstract syntax tree, a control flow graph and a data flow graph through static analysis of the smart contract; noise codes outside called external functions and variable positions are pruned for different representation modes, and features of the noise codes are obtained; abstract syntax tree features are learned through an extended recurrent neural network, and control flow graph and data flow graph features are learned through a graph attention network; and finally, features obtained through fusion of the three kinds of features are used to detect smart contract vulnerabilities. The smart contract vulnerability detection method based on multi-view learning can more comprehensively capture indicative features of vulnerabilities from codes, simplify redundant noise, and improve the performance and effect of smart contract vulnerability detection.