5results about How to "Accurate features" patented technology

The invention belongs to the field of protein fluorescence intensity prediction, and particularly relates to a protein sequence fluorescence intensity prediction method, system and device and a storage medium, and the method comprises the steps: carrying out the feature extraction of a training data set, combining random noise, training a generator and a discriminator, obtaining a generative adversarial model, supplementing data through the generative adversarial model, and obtaining a protein sequence fluorescence intensity prediction result. The method comprises the steps of obtaining a prediction data set, then extracting semantic features of the prediction data set, performing local mode enhancement and fusion on hidden layer features to obtain final features, inputting the final features into a prediction network to train the prediction network to obtain an optimal prediction network, and inputting an actual protein sequence into the optimal prediction network. The fluorescence intensity corresponding to the actual protein sequence is obtained. The method has the effect of improving the accuracy of predicting the fluorescence intensity of the protein sequence.

The application discloses a visual information recognition method and system based on eye movement attention, a medium and equipment, and relates to the technical field of visual analysis. Eye movement data is obtained through an eye jump experiment, and after processing, the eye movement data first enters a multi-head attention unit to calculate the self-attention weight of the eye movement data to perform feature selection in the first stage, then enters a local attention unit to further extract local attention features and obtain key information of the eye movement data, and finally, the data information is added through an adding unit, so that the extracted features are more comprehensive and more accurate. In the case of limited eye movement data, the key information in the features is maximized to more accurately detect visual information.

The application discloses an intention recognition method, device, equipment and medium. In the embodiment of the application, after a text matrix corresponding to a control text is input into an intention recognition model, the intention recognition model performs feature extraction operations on the text matrix based on a preset number of feature extraction methods, and a preset number of feature matrices are obtained. Compared with the prior art in which only one feature extraction method is used to perform a feature extraction operation on a text matrix, the features extracted based on the preset number of feature extraction methods are more accurate, which effectively improves the accuracy of the intention recognition model in identifying an intention and improves the user experience.

Embodiments of the present application relate to a feature extraction method and device, electronic equipment and storage medium. The method comprises: extracting a feature map of a target image; inputting the feature map into a trained channel attention feature extraction model and a spatial attention feature extraction model respectively to obtain channel attention features and spatial attention features of the target image; using a preset weighted distance feature fusion algorithm and a weighted product feature fusion algorithm to perform feature fusion on the channel attention features and the spatial attention features respectively to obtain weighted distance fusion features and weighted product fusion features; determining a self-attention parameter based on the weighted distance fusion features and the weighted product fusion features; and determining self-attention features of the target image based on the feature map and the self-attention parameter. Thus, the accuracy of feature extraction is improved.

The utility model discloses an automatic sampling device for raw materials, which relates to the technical field of material sampling and comprises an incoming material conveying belt, a sampling station, an automatic sampling device and a sampling device. A transfer belt is arranged at the tail end of the sampling station and communicated with the sample preparation system; a transfer conveying belt is arranged on one side of the sampling station and is communicated with the material returning field; sampling grooves are formed in the bottoms of the sampling stations. The device is matched with a system sampling method to realize automation of a series of links from raw material conveying to sampling, transferring, sample preparation and the like; in the whole process, the influence of human factors on a sampling result is reduced, system errors caused by manual misoperation or manual intervention are avoided, the fairness and objectivity of the sampling result are improved, the labor intensity of workers is reduced, the working efficiency is improved, and the labor intensity of the workers is reduced. The method ensures that the taken samples can more accurately represent the overall characteristics of bulk materials, and provides a more reliable basis for trade settlement.