7results about How to "Enhanced representation" patented technology

The application belongs to the technical field of bridge health monitoring, and particularly relates to a structure damage identification method based on a deep reconstruction network and multi-dimensional feature fusion, which comprises the following steps: obtaining an acceleration signal of a target bridge structure; inputting the acceleration signal into a Res-UNet-AE autoencoder to obtain a reconstructed signal, and calculating a reconstruction error and a signal-to-noise ratio according to the reconstructed signal; inputting the acceleration signal and the reconstructed signal into a perception autoencoder to obtain a perception index; fusing the reconstruction error, the signal-to-noise ratio and the perception index to obtain a three-dimensional damage feature space; and performing unsupervised clustering and identification according to the three-dimensional damage feature space to obtain a damage category identification result of the target bridge. The application can realize accurate differentiation of different working conditions of a structure without relying on any damage label.

The application discloses a small target detection method and system for unmanned aerial vehicle aerial image, relates to the technical field of computer vision and unmanned aerial vehicle target detection, and generates a C2f_iAFF module by embedding an iterative attention feature fusion mechanism iAFF in a C2F convolution module of a YOLOv8 network, trains the C2f_iAFF module by using an STDIoU loss function which is optimized based on MPDIoU and is suitable for small target detection characteristics, adds a small target detection head to an original network structure of the YOLOv8 to generate an improved network structure, and finally obtains a small target detection model which is used for detecting unmanned aerial vehicle aerial images, obtaining a small target detection type and result, and improving the precision and speed of small target detection.

The present application relates to a kind of non-cooperative communication signal modulation identification method based on multiscale feature fusion extraction, belong to cognitive radio communication technical field, including generating the time domain signal of communication signal under different modulation style and being preprocessed to obtain multiple baseband IQ signal as sample data, with corresponding modulation style as sample label composition communication signal dataset;Initialization multiscale feature fusion extraction network model, utilize the modulation signal dataset to train the multiscale feature fusion extraction network model, obtain trained multiscale feature fusion extraction network model;Real-time acquisition non-cooperative communication signal time domain signal and pre-processing, input the trained multiscale feature fusion extraction network model, obtain the identification result of corresponding time domain signal modulation style.Accurate and efficient identification of non-cooperative communication signal modulation style is realized.

The application provides a human pose estimation method and system based on dynamic perception sparse attention, wherein the method steps comprise: preprocessing original image data to obtain a feature map of a person in the image; performing regional division and linear projection processing on the feature map according to a preset block size to obtain query, key and value tensors, and deriving a relevant region affinity graph in the feature map through a primary routing algorithm; filtering out the most valuable region in the relevant region affinity graph, performing attention calculation, obtaining a corresponding weight matrix, and inputting the weight matrix into a multi-layer perception machine to extract pose information; and inputting the pose information into an encoder to refine the pose information, and generating a key point heat map based on the pose information to detect a human pose. In this way, the processing capability for occluded poses is further enhanced, the accuracy of human pose estimation is overall improved, and the computational complexity is reduced.

The application relates to the technical field of wireless communication network optimization, and discloses a cell interference investigation method based on AI and big data. The method comprises the following steps: a three-dimensional space-time index is established and is aggregated to form a space-time feature cube, the space-time feature cube is sliced along a time axis and is aggregated, and a dynamic graph snapshot sequence is output; based on the dynamic graph snapshot sequence and the space-time feature cube, an enhanced node feature vector is generated through multi-modal gating mechanism fusion, and an enhanced dynamic graph snapshot sequence is acquired; a causal direction bias of a structural causal model is injected into a message passing process of a graph neural network, a jamming probability of a candidate jamming cell is output based on the enhanced dynamic graph snapshot sequence, and after the confidence is corrected through counterfactual verification, a root cause positioning tuple is output. The application enhances features through multi-modal gating fusion, integrates a causal model to construct GNN causal attention, realizes interference tracing through counterfactual correction, and relies on digital twin deduction and SPC verification closed loop to avoid strategy issuing derived faults.

This invention provides a deep learning method for reconstructing sea surface current fields based on multi-source air-sea spatiotemporal features, relating to the field of marine data processing. Specifically, it includes: acquiring multi-year continuous sea surface current velocity data and multi-source environmental feature data for a target sea area; constructing a full-field training sample in a continuous time series manner, using multi-source environmental features from multiple consecutive historical moments as input to candidate deep learning models, and using the full-field data of the eastward and northward current velocity components at a future target time as prediction labels; constructing velocity moduli using the eastward and northward current components; training candidate deep learning models using a joint loss function; evaluating candidate deep learning models, selecting the optimal model, and using the optimal model to output the full-field current velocity results at the target time. The technical solution of this invention overcomes the problem in existing technologies of difficulty in obtaining long-term, large-scale, spatially continuous sea surface current field data.

The application provides an image editing method and system based on a visual prompt pair, through given visual prompt pairs containing example source images and example edited images, a progressive structured training strategy is used to make the model learn the conversion relationship between the two and migrate to the query source image, and a condition compression and reuse mechanism is combined to generate edited images that are highly consistent with the visual prompt and retain the core features of the query image. The progressive structured training strategy of the application includes two stages of text decoupling training and superior-inferior comparison refinement, and the condition compression and reuse mechanism reduces the computational overhead by downsampling the example source image, the example edited image and the query image and reusing the approximately invariant features in the inference stage. The application can efficiently perform image editing based on the visual prompt pair in the inference stage, significantly improving the high-resolution generation speed while achieving high-fidelity following of the visual prompt.