8results about How to "Rich expressive ability" patented technology

The invention belongs to the technical field of data generation, and discloses a virtual character training data generation method and system, and the method comprises the steps: analyzing a virtual character, and constructing the attribute, expression mode and application scene of the virtual character; based on the attribute, the expression mode and the application scene of the virtual character, training the large language model, and evaluating the generation effect after training; dynamically adjusting the proportion of the expression modes in the labeling data set according to the evaluation result, and updating the large language model based on the adjusted proportion; and according to the attribute, the expression mode and the target application scene of the target virtual character, generating corresponding dialogue content data by using the updated large language model, and obtaining virtual character training data. The method not only supports complex virtual character setting, but also has the advantages of high mobility and efficient preparation of multi-virtual character data.

The invention relates to a Transform and RF fused power load prediction method, which comprises the steps of obtaining short-term power load data to be predicted in a target area, inputting the short-term power load data into a trained random forest network RF after clustering and decomposition, and outputting a power load prediction result. The RF training process comprises the following steps: acquiring power load historical data and corresponding historical weather data of a specific area, and preprocessing to obtain a first training data set and a second training data set with labels respectively; clustering and decomposing the first training data set with the label and the second training data with the label based on the power load historical data and the time sequence characteristics of the corresponding historical weather data; respectively inputting the clustered and decomposed power load data into a multi-modal time sequence prediction converter to obtain a feature sequence; and the feature sequence is transmitted to RF for regression prediction, regression loss is calculated, and the random forest network is iteratively optimized through the regression loss. The accuracy of a power load prediction result can be obviously improved.

The invention relates to the technical field of image recognition and processing, and discloses an emotion-driven 2D supernatural digital human video generation system of a multi-modal model, and the system comprises a voice input module which is used for receiving a voice signal of a user and then converting the voice signal into a digital signal; the voice preprocessing module is used for carrying out noise elimination and normalization processing on the voice signals; the voice emotion analysis module extracts emotion features based on the digital signals of the voice preprocessing module; the multi-modal generation module is used for mapping the emotional characteristics into digital human gestures and digital human population type actions; and the video generation module is used for synchronizing the gesture and the mouth shape action with the voice signal and generating a 2D digital human video. According to the method and the device, the speech emotion features are accurately mapped into the gestures and mouth shape actions of the digital human through the multi-modal generation model, the actions highly matched with the emotion types, the emotion intensity and the mood features are generated, so that the expression effect with rich emotions is visually presented, and the user experience is more realistic.

The present invention discloses a GAN enhanced magnetic induction imaging method and system based on complex-valued convolution, comprising: S1, collecting voltage sequence data, constructing a complex-valued neural network model, inputting the voltage sequence data into the complex-valued neural network model for training, and obtaining a preliminary conductivity distribution image; S2, constructing a generative adversarial network model, inputting the preliminary conductivity distribution image into the generative adversarial network model for training, and obtaining a generator for image enhancement; S3, inputting the preliminary conductivity distribution image into the generator, and obtaining a high-precision target conductivity distribution map. The present invention uses the generative adversarial network model as an image optimization module to perform image enhancement on the output of the complex-valued convolutional network, making full use of the complex-valued characteristics of the voltage sequence data, improving the training efficiency of the neural network and the accuracy of conductivity reconstruction, and thus improving the resolution and accuracy of the final image.

The present invention discloses a detection model, method, device and storage medium for aerial insulator self-explosion defects. The detection model includes a backbone network D-Darknet53, a feature mining module and a four-scale prediction layer; the backbone network D-Darknet53 is used to extract image feature information of different scales and different levels of insulator defect images; the feature mining module is used to fuse image feature information of different scales and different levels of insulator defect images to obtain an insulator defect feature image with rich feature information; the four-scale prediction layer is used to predict the four-scale insulator defect feature images after feature fusion to obtain the best prediction frame of the insulator defect. The present invention can accurately identify and locate the self-explosion defects of aerial insulator strings in overhead transmission lines, and can solve the problems of slow detection speed and low detection accuracy of insulator self-explosion defects using deep learning.

The application relates to the technical field of direction of arrival estimation, and specifically discloses a cross-platform complex domain feature enhancement coherent super-resolution DOA estimation method, which comprises the following steps: firstly, a complex domain CVSIMO learning model is constructed; sampling data is preprocessed; forward propagation of the CVSIMO learning model is carried out; backward propagation and parameter optimization of the CVSIMO learning model are carried out; data reconstruction and signal separation are carried out; cross-platform super-resolution DOA estimation is carried out; and finally, model simulation experiments are carried out. The cross-platform complex domain feature enhancement coherent super-resolution DOA estimation method can realize feature mining of multiple point source signals, converts a multi-source estimation problem into a single point source DOA estimation problem by using a complex domain neural network model, enhances the features of coherent signals by using the advantages of the complex domain neural network, improves the performance and precision of the super-resolution DOA estimation algorithm, can also realize data separation of multiple coherent signals and cross-platform DOA estimation, and realizes real angle estimation through ingenious feature solving.

The invention discloses a remote sensing image scene classification method, and the method comprises the steps: obtaining an image of a remote sensing scene through Internet search, and carrying out the data enhancement preprocessing of the image; a matrix convolution maximum fusion module RCMF is introduced into the VanilaNet-5 model, and a rectangular convolution kernel convolution layer addition fusion and original input feature splicing method is adopted; a region selection module AS is added behind an RCMF, and a sequential polarization self-attention mechanism SPS is introduced; then the AS combines the SPS with a mini-region convolution module MRC to perform feature multiplication fusion; secondly, inputting the preprocessed image data into the improved model for training and testing to obtain an optimal model; and finally, inputting a to-be-classified image into the optimal model to obtain a classification result. The remote sensing image scene classification method fusing the VanilaNet-5 network and the rectangular convolution provided by the invention has a good application prospect.

This invention belongs to the field of character recognition technology and provides a handwritten letter recognition method based on piezoresistive signal detection, including the following steps: collecting the temporal piezoresistive signal during the letter writing process as raw data; preprocessing the raw data to obtain training data; constructing a recognition model, inputting the training data into the recognition model to extract temporal feature vectors, and performing classification training; using the trained recognition model for forward inference to confirm the letter category; this invention accurately captures changes in writing force and temporal logic based on piezoresistive electrical signals, significantly improving the accuracy of handwritten letter recognition.