124results about How to "Enhance feature expression" patented technology

The invention provides a time sequence prediction method and system for a KAN enabling graph attention parallel loop network, and relates to the technical field of time sequence prediction, and the method comprises the steps: obtaining time sequence input data; inputting the time sequence input data into a feature extraction layer based on fast Fourier transform and discrete wavelet transform, and preprocessing the time sequence input data by the feature extraction layer to obtain spliced feature extraction data; inputting the spliced feature extraction data into a feature aggregation layer of graph attention based on KAN, and preprocessing the spliced feature extraction data by the feature aggregation layer to obtain feature aggregation data; and inputting the feature aggregation data into a time sequence prediction model of the parallel gating circulation unit network based on KAN and attention, and outputting time sequence prediction data by the time sequence prediction model. According to the method, the calculation complexity of time sequence prediction is reduced, and the time sequence prediction efficiency and accuracy are improved.

The invention provides a three-dimensional seismic fault identification method based on deep learning, and the method comprises the following steps: (1), carrying out the automatic modeling, generating three-dimensional seismic data and an accurate fault label, obtaining a three-dimensional seismic image data set, dividing the data set into a training set and a test set, and carrying out the recognition of the three-dimensional seismic fault; (2) training an improved 3D-Unet convolutional neural network of a two-channel structure or an AG-3D-Unet network fused with an attention mechanism and residual learning by taking the three-dimensional synthetic seismic data of the training set and a label of the three-dimensional synthetic seismic data as input, and verifying by using a test set to obtain a trained seismic fault recognition model, (3) inputting real seismic data, and obtaining a seismic fault recognition result. And outputting a fault identification result for the input to-be-predicted image through the identification network. According to the method, the definition, the fault continuity and the accuracy of fault identification are obviously improved, and the method has high practical value in actual seismic data fault interpretation.

The invention belongs to the technical field of image recognition, and particularly relates to a skin disease classification recognition system based on deep learning, which comprises a skin image acquisition module, a skin image preprocessing module, a skin state classification module and a state classification optimization module, according to the system, a skin state recognition model is constructed, and a multi-head self-attention mechanism is introduced, so that skin state differences can be distinguished; local features are extracted through a space exploration CNN block, an AssemFormer block learns a global dependency relationship, the local features and the global features are fused, and a lesion mode can be understood; scale variant attention blocks are used for highlighting skin features of suspected lesion areas, Monte Carlo attention blocks are used for carrying out random sampling pooling calculation on feature representation, attention map enhancement blocks are used for generating attention maps, skin state categories are output, and skin state prediction labels are generated for each skin image, so that skin lesion details can be highlighted; quantitative reference is provided for medical staff, and skin state judgment is effectively assisted.

The invention discloses a hyperspectral and panchromatic image fusion method based on feature enhancement channel shuffling, and the method comprises the steps: obtaining a hyperspectral image and a panchromatic image, carrying out the preprocessing of the hyperspectral image and the panchromatic image, and correspondingly obtaining a preprocessed hyperspectral image and a preprocessed panchromatic image; a hyperspectral image feature enhancement module based on matrix rank constraint performs feature extraction on the preprocessed hyperspectral image to obtain hyperspectral image features; a filtering-based panchromatic image feature extraction module performs feature extraction on the preprocessed panchromatic image to obtain panchromatic image features; splicing the hyperspectral image features and the panchromatic image features to obtain input features of an image feature fusion part; and carrying out feature fusion on the input features based on multi-scale convolution, extracting features of different scales, and carrying out deep fusion on the features of different scales based on a deep and shallow fusion module of channel shuffling to obtain a final fusion result. According to the invention, the image fusion effect is further improved.

The invention discloses a big data analysis-based power distribution network planning decision optimization method and system, and relates to the technical field of power grids, and the method comprises the steps: constructing a system overall architecture, and carrying out the preprocessing of the constructed system overall architecture according to an improved deep learning preprocessing method; and a multi-source data fusion method is introduced, and the preprocessed system overall architecture is improved through a multi-objective optimization algorithm to complete power distribution network planning decision optimization. The training efficiency of the model is improved. The multi-target optimization algorithm processes multi-source data through a momentum modified Adam optimizer and an attention fusion network, and the feature expression ability and the model generalization ability are improved.

The invention provides an image recognition and positioning method for a component embedded part based on target detection, and the method comprises the steps: improving a YOLOV8 deep learning network model, adding a CBAM attention mechanism module in a Backbone network, and replacing an SPPF module with an ImproveSPPF module; the Conv module is replaced by a GSConv module in the Neck network; the bounding box regression loss function is optimized in the Head network; and performing embedded part identification and positioning on the plane image data set of the prefabricated part by using an improved YOLOV8 deep learning network model, and calibrating actual coordinates of the center position of the embedded part by using a two-dimensional code pixel size calibration technology. By means of the improvement, the recognition efficiency and precision of the embedded parts such as the wire box and the embedded pipe in the prefabricated part are effectively improved, and precise positioning of the embedded parts is achieved.

The invention discloses a strawberry shape discrimination method based on an improved YOLOv8 model, which belongs to the technical field of image processing, and comprises the following steps: constructing a strawberry data set through strawberry image acquisition, and carrying out preprocessing operations such as data annotation, data enhancement, data division and the like; a strawberry shape discrimination model based on improved YOLOv8 is constructed, a C2f module in a backbone network is replaced by a CSP structure based on lightweight convolution PConv, a C2f module in a neck network is replaced by an attention mechanism module fusing an EMA attention mechanism and an iRMB inverted residual structure, and a DyHead detection head module is introduced into a head network part; and network weight parameters are obtained through model training, and strawberry shape target discrimination is realized. The strawberry shape can be accurately judged in a complex environment, and automatic strawberry picking and intelligent monitoring are assisted.

This invention provides a method and apparatus for correcting sea surface temperature (SST) predictions based on spatiotemporal axial attention. The method includes inputting target SST data into a target SST prediction correction model, comprising a convolutional input layer, an encoder, and a decoder. The convolutional input layer performs feature extraction, temporal encoding, and positional encoding on the target SST data to obtain a target feature vector. The encoder performs attention calculations on the target feature vector in three dimensions to obtain a first target output vector. The decoder outputs the target SST prediction result based on the first target output vector and historical prediction values ​​output by the decoder. Temporal and positional encoding are performed during the model input stage to enhance the representation of temporal and positional information contained in the original data. By performing attention calculations on the target feature vector in the spatiotemporal, longitude, and latitude dimensions respectively by the encoder, effective fusion of features in different dimensions is achieved, further improving feature representation capabilities and increasing the accuracy of SST prediction.

The invention discloses a PHSM-YOLO-based multi-target wearing identification method. The PHSM-YOLO-based multi-target wearing identification method comprises the following steps: constructing a PHSM-YOLO target detection model used for identifying whether a constructor correctly wears a safety appliance; collecting a target image containing constructors in the construction environment; and inputting the target image into a PHSM-YOLO target detection model, and outputting a wearing identification result of the safety wearing tool of each construction worker in the target image by the PHSM-YOLO target detection model. According to the multi-target wearing identification method, the wearing of the safety wearing tool can be identified on the target image containing the construction personnel through the constructed PHSM-YOLO target detection model, so that the personal safety of the construction personnel is guaranteed.

This invention relates to the field of smart agriculture information technology, and discloses an agricultural pest and disease prediction system and method based on multimodal data fusion and edge AI, comprising: a data acquisition module, an edge AI analysis module, and a communication interaction module; the data acquisition module is used to collect agricultural monitoring data containing agricultural environmental information and crop status information; the edge AI analysis module is used to call a pest and disease prediction model that integrates multimodal agricultural feature extraction to perform multimodal joint feature extraction on the agricultural monitoring data containing agricultural environmental information and crop status information, and infer pest and disease prediction results based on the multimodal joint features, and generate pest and disease early warning information based on the pest and disease prediction results; the communication interaction module is used to upload the agricultural monitoring data collected by the data acquisition module and the pest and disease prediction results and pest and disease early warning information output by the edge AI analysis module to a cloud platform. This invention enables agricultural pest and disease prediction on resource-constrained edge devices.

The invention discloses an ocean sub-mesoscale process instance segmentation method based on frame supervision, and relates to the field of ocean remote sensing information processing. The invention aims to solve the problem of low segmentation precision of the existing frame supervision instance segmentation method. The method comprises the following steps: forming a first training set by using a chlorophyll concentration remote sensing image and a frame label of an ocean sub-mesoscale process; training a teacher model by using the first training set, and outputting an initial prediction mask set by the trained teacher model; performing mask correction on the initial prediction mask set to obtain a pseudo mask; forming a second training set by using the chlorophyll concentration remote sensing image, the pseudo mask and the frame label of the ocean sub-mesoscale process, and training and optimizing the student model by using the second training set to obtain a trained student model; and inputting a chlorophyll concentration remote sensing image to be tested into the trained student model to obtain an ocean sub-mesoscale process instance segmentation result. The method is used for obtaining the ocean sub-mesoscale process region.

The application belongs to the technical field of target detection, and discloses a UAV image target detection method based on an improved RT_DETR model, which introduces an FAPPA module in a backbone network, effectively alleviating the information loss problem of small target features in a deep network. The method also replaces the feedforward neural network of the AIFI module in the encoder with an EDFFN module, which selectively enhances high-frequency features such as edges and textures by using a patch_wise FFT strategy and learnable frequency domain weights. In addition, the method also uses a Zoom_cat module to realize adaptive alignment of multi-scale features, and uses the convolution_attention dual-path architecture of the CAFMFusion module to fuse local details and global semantics, thereby fully utilizing complementary information at different levels. The method can significantly improve the UAV image target detection capability and also identify small target features in a complex background.

The invention relates to an unmanned vehicle obstacle identification method and system, and the method comprises the steps: obtaining surrounding point cloud data in real time through employing a laser radar sensor equipped on an unmanned vehicle, carrying out the noise filtering and down-sampling of the point cloud data, and calculating the normal vector and local point cloud density of each point in the point cloud data; for each target point, calculating the included angle between the target point and the normal of the neighborhood point, calculating the normal consistency weight of the neighborhood point through the normal included angle, calculating the statistical filtering weight of the neighborhood point according to the local point cloud density, and taking the product of the normal consistency weight and the statistical filtering weight as the weight of the neighborhood point; calculating a fast point feature histogram of the target point by using the weight of the neighborhood point, the simple point feature histogram of the neighborhood point and the simple point feature histogram of the target point; and segmenting the point cloud through the positions of the points in the point cloud and the fast point feature histograms, classifying the segmented regions by using a classifier according to the fast point feature histogram of each segmented region, and determining the type and the distance of the obstacle.

The invention discloses a visual detection method for the welding quality of a lithium battery pole, and the method comprises the steps: S1, obtaining a lithium battery pole welding image and a pixel-level marking image corresponding to each welding image, and forming a sample set; step S2, carrying out preprocessing on the training sample set train; s3, building a visual detection model; s4, training the visual detection model, storing a training weight file of the visual detection model, and generating the visual detection model; s5, inputting a to-be-tested lithium battery pole welding image into the trained visual detection model to obtain a segmentation result image, and processing the segmentation result image to obtain a semantic segmentation region result image; and S6, performing post-processing on the semantic segmentation region result map, processing and segmenting internal mixing and undersize pixel filtering, and obtaining a final semantic segmentation type region result map. According to the method, the defects of low detection precision, insufficient generalization adaptive capacity, low detection speed and multiple required samples in the prior art are overcome.

The invention relates to the technical field of three-dimensional sensing, in particular to a point cloud self-supervised learning method based on multi-feature sensing and auxiliary reconstruction. The method comprises the following steps: S1, grouping point clouds of a random mask strategy; s2, local mark multi-feature fusion embedding is carried out; s3, pre-training the backbone network of the auto-encoder; s4, performing GaussPoint enhancement in a fine tuning stage; s5, auxiliary branch reconstruction in a fine tuning stage; and S6, carrying out multi-task coverage classification segmentation evaluation. According to the method, a Point-MAR network model is constructed on the basis of a mask self-encoding normal form by combining a multi-feature perception fusion embedder, an auxiliary reconstruction branch and a GaussPoint data enhancement method, feature expression, geometric perception and generalization capabilities are remarkably improved, and the method shows good competitiveness compared with numerous mainstream models in downstream tasks.

The invention discloses a power equipment fault diagnosis method and system based on multi-source data fusion. The method comprises the following steps: extracting linear features of data sources from preprocessed multi-source data through an improved principal component analysis (PCA) algorithm; fusing the linear features of the multiple data sources through a weighted fusion strategy, and constructing a fault diagnosis model in which a bidirectional long-short-term memory network BiLSTM and a convolutional neural network CNN are mixed based on an attention mechanism; and the pre-trained fault diagnosis model is used to carry out power equipment fault diagnosis, and a fault type identification result and a fault degree evaluation value are output. According to the invention, through multi-source data acquisition, hierarchical fusion and a hybrid diagnosis model, accurate and real-time diagnosis of power equipment faults and quantitative evaluation of fault degrees are realized; according to the method, the BiLSTM and CNN hybrid model based on the attention mechanism is adopted, the spatial information and the time sequence information of the features are captured at the same time, key features are highlighted, and the accuracy of fault diagnosis is improved.

The invention discloses a wake detection model construction method based on frequency domain multi-scale perception, which relates to the technical field of ocean observation, is used for capturing weak wake features to carry out sea surface internal wave wake detection, and comprises the steps of constructing a wake image data set, constructing a wake detection model by taking an RT-DETR model as a baseline model, and constructing a multi-scale texture information selection network. A backbone network of the baseline model is replaced, a frequency domain multi-scale attention perception module is constructed, an AIFI module of the baseline model is replaced, a golden cudgel convolution down-sampling module is adopted to replace a standard convolution down-sampling module of the baseline model, training and optimization are performed on the wake detection model based on the wake image data set, and a target detection result is obtained. According to the method, the wake detection model AUVRT-DETR is constructed, the detection precision is improved while the advantage of light weight is kept, the optimal balance is achieved between the detection precision and the calculation efficiency, and the model parameter quantity and the calculation overhead are remarkably reduced while the highest detection precision is achieved.

A method and system for super-resolution reconstruction of infrared images based on convolutional neural networks, relating to the field of electronic digital data processing, is disclosed. The method includes: inputting a low-resolution infrared image into a preset convolutional neural network to obtain multi-layer feature maps of the low-resolution infrared image; scaling the feature maps of different layers in the multi-layer feature maps according to a preset ratio and then stitching and fusing them to obtain a fused feature map; generating feature vectors from the fused feature map using global average pooling, and transforming the feature vectors using a fully connected layer to obtain attention weights; weighting the attention weights with the fused feature map to obtain an enhanced feature map; and performing upsampling reconstruction processing on the enhanced feature map using an upsampling structure, introducing a residual connection structure during the upsampling reconstruction process to generate a high-resolution infrared image. Implementing this method generates high-resolution infrared images with more detail.

The invention discloses an electric welding machine intelligent operation and maintenance management method and system based on machine learning, and belongs to the technical field of operation and maintenance management, and the method comprises the steps of electric welding machine operation and maintenance data integration, electric welding machine fault detection, electric welding machine residual life prediction and electric welding machine intelligent operation and maintenance management. According to the scheme, the original importance score is calculated based on the Gini impurity reduction amount, the weighted importance score of the features is obtained in combination with the fault correlation coefficient, the key feature set is constructed based on the accumulated score, the comprehensive confidence of the decision tree is obtained, the class weight compensation factor is introduced for correction, and the accuracy and reliability of fault detection are improved; based on the calibrated degradation complexity, a dynamic window increment is calculated, a multi-scale window set is constructed, a multi-scale sample is generated, a full connection graph is constructed, and weighted frequency domain features are obtained in combination with the degradation stage, so that the prediction precision of the remaining service life is improved, and a reliable basis is provided for operation and maintenance management of the electric welding machine.

This application belongs to the fields of artificial intelligence and finance, and relates to a semantic-based text classification method. The method includes inputting a training sample set and a knowledge graph into a knowledge-enhanced language model to obtain knowledge-enhanced text semantic feature vectors; inputting the text semantic feature vectors into a capsule network model to output classification prediction results; calculating the loss value between the predicted classification result and the classification label; adjusting the model parameters based on the loss value to output a model to be validated; validating the model to be validated using a test sample set to obtain a text semantic classification model; and inputting the text to be classified into the text semantic classification model for classification. This application also provides a semantic-based text classification device, computer equipment, and storage medium. Furthermore, this application relates to blockchain technology, allowing the classified text dataset to be stored in the blockchain. This application can effectively identify multi-labeled text, improving the efficiency and accuracy of text classification.