3results about How to "Strong feature extraction ability" patented technology

This application relates to the interdisciplinary field of deep learning and protein analysis, specifically to a method, apparatus, device, and medium for analyzing multi-target proteins in obesity based on deep learning. The method includes: acquiring biological sample data and preprocessing the biological sample data to obtain preprocessed biological sample data; the preprocessed biological sample data includes protein expression data and obesity classification labels corresponding to the protein expression data; the protein expression data includes multiple protein expression features; constructing an initial deep learning model for analyzing multi-target proteins in obesity based on a multilayer feedforward neural network; training the initial deep learning model based on the preprocessed biological sample data to obtain a deep learning model for analyzing multi-target proteins in obesity; and determining multiple protein expression features related to obesity based on the deep learning model for analyzing multi-target proteins in obesity.

This invention relates to the field of deep-sea signal processing and recognition technology, and in particular to a method for recognizing weak reverberation signals in the deep sea based on a deep learning model. The method includes: collecting reverberation data containing weak signals using underwater sensors; removing sensor noise and high-frequency interference from the reverberation data using a wavelet threshold denoising algorithm; normalizing the reverberation data to map its signal amplitude to a preset range; constructing a convolutional neural network model containing convolutional layers, pooling layers, and fully connected layers; dividing the normalized reverberation data into training, validation, and test sets; inputting the weak reverberation signal to be identified into the trained convolutional neural network model after denoising and normalization; and outputting the signal and recognition result from the convolutional neural network model. This invention eliminates a large amount of tedious manual feature engineering, reduces human intervention, and improves recognition efficiency and accuracy.

The application discloses a kind of HDR image reconstruction method, system, equipment and medium, it is related to computer vision image enhancement field, the method includes: obtaining the LDR image of different exposure degree under target scene;Each LDR image is mapped to HDR domain using gamma correction, obtain the gamma correction image of corresponding exposure degree;LDR image and gamma correction image set under target scene are input into image reconstruction model, obtain the HDR image of target scene;Image reconstruction model is obtained using training data to the deep learning model training;Deep learning model includes: feature extraction module and HDR reconstruction module;Feature extraction module is used to extract the double-channel feature containing channel information and spatial information to the input image;HDR reconstruction module is used to reconstruct the input image based on three-pass residual block according to double-channel feature, obtain corresponding HDR image.The application can realize end-to-end ghost-free high-quality HDR image reconstruction.