4 results about "Class discrimination" patented technology

This invention proposes a method for quality evaluation and multi-class discrimination of yam based on PCA and LDA, belonging to the technical field of agricultural product quality evaluation. The method includes the following steps: A) Constructing a discrimination model, specifically including A1) Data acquisition; A2) Data preprocessing and feature selection; A3) Constructing a linear discriminant model; B) Distinguishing yam categories, B1) Measuring 12 original indicator data of the sample to be tested according to the method described in A1); B2) Calculating the 12 original indicator data x... j Standardized to Z j B3) Z j Substitute the values ​​into the LDA discriminant function to calculate the values ​​for each type of yam; compare them, and the category corresponding to the maximum value is the discrimination result. When users apply the discrimination method of this invention, they do not need to understand the principle of PCA; they only need to perform simple standardization and a single linear combination calculation to complete the classification, which greatly reduces the threshold for using the model and the computational cost.

The application provides a kind of based on visual identification inference and the class incremental learning method and system of data playback, the method includes: S1, the pre-training main stem of visual identification image is frozen, and bottleneck type residual adapter is introduced;S2, the causal latent variable space is constructed, the inter-concept dependency is learned, and the cross-task causal structure is continuously stable through the plastic updating mechanism and sparse constraint;S3, original sample is not stored, only the statistical distribution of historical class in semantic and causal space is saved, and pseudo sample is generated to realize data-free knowledge playback;S4, progressive weight scheduling and inference period uncertainty fusion mechanism are designed, and dynamic balance new and old knowledge is selected reliable branch output to realize stable prediction.The application can simultaneously maintain old class discrimination ability, support new class effective learning and have the class incremental learning method of reasonable forgetting mechanism under the premise of not saving old data.

This invention provides a method, system, and application for interpreting all features of remote sensing images by integrating zoning control and hierarchical extraction. By innovatively introducing a zoning control network constructed from real geographic elements such as roads and waterways, the large-scale image interpretation task is decomposed into multiple independent geographic units, effectively avoiding the problem of extraction results contradicting geographical common sense. Simultaneously, this invention employs a hierarchical extraction mechanism, extracting features class by class within each zoning according to visual saliency. Each dedicated model only needs to learn to recognize a single or a few categories, fundamentally improving inter-class discrimination and overall extraction accuracy. Furthermore, by encapsulating all stages—data preprocessing, zoning generation, hierarchical extraction, and result fusion—into visual, programmable tool nodes, and uniformly scheduling and executing them through a workflow engine, this completely changes the shortcomings of existing technologies, such as fragmented processes and reliance on manual coordination, significantly improving the standardization of production and engineering application capabilities.

This invention provides a method and system for grouping flue-cured tobacco using feature channel weighting and dynamic loss control, relating to the fields of deep learning and flue-cured tobacco grading. The method involves acquiring images of flue-cured tobacco from N main groups using an image acquisition device to establish a flue-cured tobacco grouping dataset, where N is a positive integer. This dataset is then preprocessed to obtain a preprocessed dataset. A flue-cured tobacco grouping classification network (TGNet) is designed and trained on the preprocessed dataset to obtain a flue-cured tobacco grouping classification model. Based on this model, the grouping results are obtained. This invention solves the technical problems of existing deep learning methods for flue-cured tobacco grouping, such as lack of key feature representation in high-scale features, limited inter-class discrimination ability, and a tendency for the model to learn from majority class samples during training. It achieves real-time classification of flue-cured tobacco groups, effectively improving the efficiency of flue-cured tobacco group classification and reducing the cost of manual grading.