4results about How to "Reduce classification error" patented technology

The application discloses a bridge collision damage rapid evaluation method based on a conditional variational autoencoder. The method first acquires a vehicle-bridge collision dataset, determines a bridge damage state category and takes it as an output label; then, the dataset is layered according to the bridge damage state; a cVAE model is trained by using a training set, high-dimensional features are reduced and purified by an encoder, and the high-dimensional features are recovered to high-dimensional features by a decoder to realize effective representation; meanwhile, end-to-end training is carried out in combination with a damage classification task, feature coding, reparameterization, feature reconstruction, damage classification, loss calculation and parameter updating are sequentially completed, grid search is used to optimize hyperparameters, and the optimal cVAE model is used for actual evaluation. The application effectively overcomes the problems of traditional methods in high-dimensional bridge monitoring data processing, such as difficulty in feature extraction and insufficient classification accuracy; and has stronger generalization ability and higher recognition accuracy under the conditions of small samples and unbalanced damage samples.

The present application relates to the technical field of agricultural product origin identification and traceability, and particularly discloses an agricultural product origin identification and traceability system and method based on characteristic fingerprints, which comprises the following steps: collecting agricultural product samples to obtain a target sample set; marking the target sample set with dominant characteristic fingerprints according to the dominant characteristics of the agricultural products, and performing one-class partitioning on the target sample set according to the dominant marking result to obtain a plurality of one-class sample clusters; marking the target sample set with recessive characteristic fingerprints according to the recessive characteristics of the agricultural products, and performing two-class partitioning on the target sample set according to the recessive marking result to obtain a plurality of two-class sample clusters; comparing the one-class partitioning result and the two-class partitioning result to determine whether to start a successive verification mode or a separate verification mode; based on the successive verification mode, obtaining each verification result, and determining whether to output an actual origin traceability result according to the verification results.

The invention discloses a trade product classification method and system. The method comprises the steps of obtaining multi-modal data of a target product; the multi-modal data comprises text data and image data; screening product keywords from the text data, constructing text features, and performing first hierarchical classification on the product keywords to obtain a first classification result; according to the first classification result, selecting a target neural network from pre-trained convolutional neural networks for different categories of products, and extracting image features from the image data by adopting the target neural network; performing feature fusion on the text features and the image features to obtain multi-modal features; and according to the multi-modal features, carrying out second-level classification on the target product to obtain a second classification result. By adopting the embodiment of the invention, the advantages of different data can be fully utilized, the classification error is effectively reduced, and the practicability and reliability are improved.

This invention relates to the field of text classification technology, and more particularly to a text classification method, medium, and device. It dynamically determines the first parallel inference quantity M using computational resources, achieving a balance between computational resource utilization and inference stability. By initiating multi-threaded parallel inference, it generates M candidate output sequences containing a first intermediate semantic representation and a first text classification result, covering different inference logics of the model for the target text. This approach balances result diversity and inference efficiency, reducing classification errors caused by single-sequence inference bias. Through compliance verification and confidence assessment, it calculates a first quality score to obtain the target classification result. This not only filters sequences containing security risks or structural defects, ensuring the security of the classification process, but also distinguishes the reliability differences of the classification results, avoiding one-sided judgments that ignore reliability based solely on labels. In scenarios without real labels, it achieves objective quality quantification, ensuring the accuracy of the text classification results.