36results about How to "Rich in features" patented technology

The invention discloses an automatic fine ore sorting method and system based on XRT (X ray tomography) and hyperspectrum, which are used for realizing comprehensive fusion of ore surface and internal information by combining X-ray tomography and hyperspectral imaging technologies so as to improve the recognition precision and the sorting efficiency. XRT is used for extracting the internal structure and density characteristics of the ore, hyperspectral imaging is used for identifying the mineral composition characteristics of the ore surface, and information complementation is achieved. The system adopts a multi-modal convolutional neural network to perform independent feature extraction on XRT and hyperspectral data, and the extracted features are fused through a deep residual network. And the fused features are processed by a cross-modal attention mechanism to ensure that key features of different modals are fully integrated and optimized, and finally high-precision ore classification and sorting are realized. According to the method, the limitation of a single data source on feature extraction is overcome, the ore recognition and sorting precision is greatly improved, and the refined and intelligent development of ore sorting is promoted.

The invention discloses a rare earth mining area land utilization fine classification method for hyperspectral remote sensing images, and belongs to the field of remote sensing. Based on a hyperspectral remote sensing image of a rare earth mining area, an object-oriented feature extraction module and an object-oriented one-dimensional convolutional neural network classification model based on a multilayer attention mechanism are designed, and a rare earth mining area land utilization classification task is completed through the two modules. By combining the object-oriented and convolutional neural network, the classification precision of different land utilization categories is enhanced, and the phenomenon of salt and pepper noise in the classification result is avoided at the same time.

The invention relates to the field of astronomy transient source recognition combining astronomy and artificial intelligence, and when a transient source recognition task in mass data shot by a large-view-field high-sampling-rate telescope is carried out, an existing method generally uses a single classification model only depending on image data and is difficult to deal with complex and changeable data. The invention provides an astronomical transient source identification method for dynamically updating a multi-modal model. The method comprises the following steps: constructing the multi-modal model for classification by combining image data, a point spread function and physical information; and in combination with manual examination, selecting samples with wrong classification and possible transient source candidates, storing the samples and the possible transient source candidates as a new sample set, injecting the new sample set into the original training set, and loading the weight of the last training for retraining, so that dynamic adjustment and optimization of the model are realized. The method can effectively cope with mass data generated by a telescope with a large field of view and a high sampling rate, and ensures that the model always keeps efficient and accurate recognition performance under a complex background and weak signals.

The invention discloses genipin modified ginkgo protein and whey protein composite gel and a preparation method thereof, and belongs to the field of functional food. The method comprises the following steps: (1) uniformly mixing a prepared ginkgo protein solution and a prepared whey protein solution, and adjusting the pH value to obtain composite sol; (2) adding a genipin reagent into the composite sol, carrying out a cross-linking reaction, and after the reaction is completed, adjusting the pH value again to obtain a genipin-treated composite sol; and (3) carrying out thermal induction gel formation on the genipin-treated composite sol, taking out and cooling to obtain the genipin-modified ginkgo protein and whey protein composite gel. Animal protein and plant protein are combined through the cross-linking agent genipin, so that the prepared composite gel is more nutritional, pollution to the environment is reduced, application in food production is facilitated, meanwhile, the composite gel treated with the cross-linking agent genipin has more diversified functional characteristics, and the composite gel is suitable for being applied to food production. And the food can be provided for people with different requirements.

The present invention belongs to the technical field of vehicle positioning, and specifically discloses a vehicle positioning method based on multi-view and multi-scale feature fusion. A multi-view multi-scale pyramid fusion neural network is proposed in the method of the present invention. At the input level of the network, a multi-channel range image RIV projected by a lidar point cloud and a BEV representation generated by a normal distribution transformation are used to complement each other at global and local scales, thereby improving the overall understanding of the scene. Multi-view input effectively alleviates the limitations of a single view and improves the robustness and anti-interference of the model. At the network architecture level, a self-attention mechanism using Mamba is proposed. The present invention can not only make full use of the complementary correlations between multiple views, but also encode and integrate contextual information across multi-scale features. The global descriptor finally generated is feature-rich, thereby improving the accuracy of vehicle positioning.

This disclosure provides a special effects processing method, apparatus, electronic device, storage medium, and program product. The method includes: receiving a special effects addition operation for a target special effects file through a first interface, displaying a video editing item corresponding to the target special effects file, generating a first special effects video corresponding to the target special effects file, and displaying video preview information of the first special effects video; wherein the target special effects file includes a first video file; receiving a video editing operation for the first special effects video through the video editing item, determining a second video file based on the video editing operation, generating a second special effects video corresponding to the target special effects file based on the second video file, and displaying video preview information of the second special effects video. The technical solution of this disclosure enables the customization and editing of video files within special effects files through simple interface interaction to generate new special effects videos.

The invention discloses a method and a system for detecting rice bacterial leaf blight based on unmanned aerial vehicle spatial spectrum fusion. The method comprises the following steps: firstly, constructing a rice bacterial leaf blight detection data set; a rice bacterial leaf blight severity grading detection double-branch model is constructed, and the model adopts a spectrum-space double-branch parallel coding and fusion framework and comprises a spectrum branch module, a space branch module and a cross attention fusion mechanism module. And inputting the complete image of the rice field into the trained rice bacterial leaf blight severity grading detection double-branch model, and obtaining severity grading detection results of the bacterial leaf blight in different rice growth periods of the target field. According to the invention, deep complementation and dynamic information balance of spectrum and spatial features of the multispectral unmanned aerial vehicle are realized, so that the accuracy of disease detection and the generalization ability of the model are effectively improved.

The present application relates to the technical field of bearing life prediction, in particular to a kind of gradient composite coating sliding bearing life prediction method, comprising the following steps: step one, build the coating degradation physical simulation model of multi-field coupling;Step two, adopt active learning algorithm to build high-precision proxy model, design active learning query strategy;Step three, online monitoring and multi-domain feature extraction;Step four, real-time state mapping and life prediction based on transfer learning;Step five, prediction result output and model updating.The present application can improve the accuracy of gradient composite coating sliding bearing life prediction, greatly reduce the operation load, and improve the prediction efficiency of bearing life.

This invention discloses a weakly supervised open-pit mining site identification method based on adaptive SAM and automatic knowledge learning. The method includes: S1, collecting images of the study area and extracting three-band images using an input spectral adapter; extracting depth features using the feature extractor of the MobileSAM model; constructing a multi-scale perception and geometric adaptation module and obtaining image features by applying N² dilated convolutional units with different dilation rates to the depth features; S2, a scene classifier using predefined prototype features to guide image features in scene classification; then, a cue generator using class activation mapping to obtain a heatmap for scene classification and selecting a set of high-confidence cue points; and a pseudo-label generator constructing triplet contrast constraints; S3, the mask decoder of the MobileSAM model combining cue information and image features to perform cue decoding and obtain the open-pit mining site identification result. This invention combines a set of high-confidence cue points with image features for cue decoding to obtain efficient and high-precision open-pit mining site identification results.

The invention discloses a remote desktop protocol bearer service type identification method based on a full-load marking sequence. The method comprises the following steps: step 1, performing full-load marking on each data packet in remote desktop protocol traffic; step 2, alternately dividing the data stream into a full-load continuous packet group and a non-full-load continuous packet group; step 3, forming a load identification packet cluster by each non-full-load continuous packet group and a subsequent full-load continuous packet group; step 4, extracting intra-cluster spatial features and time features of each packet cluster; 5, representing each data stream as a load identification packet cluster feature sequence; step 6, discrete wavelet transform is carried out on the multi-dimensional load identification packet cluster feature sequence, and robust feature vectors are extracted; 7, training a Gaussian mixture model; and step 8, realizing service type identification for the to-be-identified data stream. Through the method, different service types in the remote desktop protocol flow can be accurately identified, and technical support is provided for network behavior supervision and communication governance.

The application discloses a false information detection method, device and equipment based on a propagation forest, and the method comprises the following steps: acquiring propagation data of each source blog in a blog propagation in a social network for a source blog set to be detected, wherein the propagation data comprises propagation content and propagation relationship; extracting text features of the propagation content; calculating a propagation prototype and constructing a propagation tree after dividing the source blog set into K class clusters according to the text features; constructing a propagation forest based on the propagation prototype and the propagation tree; aggregating node neighborhood representations in the propagation forest to obtain embedding representations of the nodes; calculating enhanced embedding representations of source blog nodes and subsequent propagation blog nodes according to corresponding text features and embedding representations respectively; and classifying the enhanced embedding representations of the source blog nodes and the subsequent propagation blog nodes by applying a detection model to obtain false detection results of each source blog in the source blog set. The application can improve the performance of a false information detection task.

The application provides a kind of double-link point cloud completion method, system, equipment and storage medium, method includes: the original point cloud data is respectively input into first feature extraction branch and second feature extraction branch, and first feature vector and second feature vector are respectively obtained by different network architecture;The first feature vector and the second feature vector are fused to obtain global high-dimensional semantic feature;And the global high-dimensional semantic feature is decoded to generate the target point cloud set of completion.This application uses two branches to simultaneously extract different dimensional features of point cloud, obtain more, richer high-dimensional semantic features and local features containing global point cloud information, which can improve point cloud completion accuracy and point cloud density.

The present invention discloses a high-resolution remote sensing image fusion method based on ICGAN, and belongs to the field of image fusion technology. The present invention comprises acquiring remote sensing image data to construct an ICGAN model, training the model based on the remote sensing image data using a generator and a discriminator; using the generator to generate a high-resolution multispectral image, using the discriminator to distinguish the true from the false of the output result of the generator; finally, testing the model based on the trained generator, and comparing and analyzing the test results. The present invention has a strong ability to retain the spectral information of low-resolution multispectral images and the spatial information of panchromatic images, and the spatial details and texture features of the ICGAN fused image are richer, and the effect is better than other fusion methods.

The application discloses a SAR small target detection network based on coordinate perception attention and spatial semantic context. Target detection is an important task of remote sensing image intelligent processing. Through a series of algorithms, the target on the image can be automatically positioned and recognized. Due to the imaging mechanism of SAR, there are many speckle noise points in the image, which makes it difficult to distinguish the target from the background, so compared with optical remote sensing images, the processing of SAR images is more difficult. The application can significantly improve the detection effect of small-scale targets of SAR remote sensing images, especially for the SAR remote sensing small target detection scene with complex background and serious noise interference, on the basis of effectively suppressing background noise, the detection rate and detection precision are significantly improved, the robustness and generalization ability of the model are strengthened, and the adaptability of the model to different scenes is improved.

This invention provides a method, system, and model training method for recognizing cognitive saturation, which can be applied to the field of cognitive saturation recognition technology. The method includes: performing spectral analysis on the EEG signal data of the object to be identified to obtain frequency domain EEG feature data; for any EEG channel in at least one EEG channel, obtaining graph domain EEG feature data based on the intra-channel visibility relationship between multiple EEG sample values ​​in the EEG time-series data corresponding to the EEG channel; obtaining graph domain physiological feature data based on the cross-channel visibility relationship between multiple physiological sample values ​​in the physiological time-series data corresponding to each of the multiple physiological channels included in the physiological signal data; fusing the frequency domain EEG feature data, graph domain EEG feature data, and graph domain physiological feature data to obtain multimodal feature fusion data; and determining the cognitive saturation level of the object to be identified based on the multimodal feature fusion data.

This invention discloses a method for preparing Polygonatum wine through co-fermentation of a compound microbial strain. Firstly, this invention provides a compound microbial fermentation agent composed of *Lactobacillus plantarum*, *Bacillus lactis*, and *Saccharomyces cerevisiae* in a mass ratio of 2–4:1–2:50–100. This compound microbial fermentation agent, through the synergistic effect of the compound microorganisms, effectively balances and improves the original bitterness of Polygonatum, significantly improving the overall quality of the Polygonatum wine; it accelerates the fermentation process, significantly shortens the fermentation cycle, and improves production efficiency, thereby reducing production costs. This invention further provides a method for preparing Polygonatum wine based on this compound microbial fermentation agent. This method exhibits significant effects in improving taste, increasing fermentation efficiency, enhancing product stability, reducing adverse metabolites, and improving nutritional and health benefits, thus meeting consumers' pursuit of high-quality Polygonatum health drinks.

The present application belongs to the technical field of computer vision and image processing, and aims at the technical problem that the existing technology cannot adaptively adjust the mapping behavior according to the characteristics of different image regions. A multi-layer lookup table image mapping method based on conditional perception and hybrid expert mechanism is proposed. The linear expansion of the receptive field and the diversification of the features are realized through the spatial progressive fusion architecture. The hybrid expert gating mechanism is introduced, and the contribution weights of the basic expert branch and the detail expert branch are dynamically adjusted through the conditional perception module to realize the spatial adaptive mapping. Finally, the diagonal line priority compression algorithm is used to compress the depth of all expert lookup tables, which significantly improves the spatial adaptability and storage efficiency of image mapping while maintaining extremely low inference latency.

The application provides a kind of large resolution difference non-homologous synthetic aperture radar sea ice drift detection method, comprising: obtaining training sample, constructing super-resolution reconstruction network, and using super-resolution reconstruction network uniform resolution;Based on the high-resolution image pair obtained by super-resolution reconstruction network, feature extraction and coarse matching are carried out;Based on the features after feature extraction and coarse matching, remove the error matching points;Calculate the sea ice drift vector field, obtain complete non-homologous SAR image sea water drift information.The application can realize the full-automatic detection of sea ice drift, and use the advantages of non-homologous SAR high spatial resolution and high time resolution, obtain high-resolution, quasi-real-time sea ice drift field, meet the demand of realizing high-resolution sea ice drift automatic detection from massive SAR data.

This invention provides a multi-branch motor imagery EEG signal feature fusion classification method based on data transformation. It aims to expand the network structure width (i.e., network branches) by converting EEG data into different input formats and using multiple branch networks for processing. Gram angle fields are used as the input format for the new converted data, providing richer features compared to depthwise separable convolution and time-frequency maps. This improves the completeness of feature extraction, ensuring that different network branches possess distinct salient features, and the extracted features are complementary. Converting to different data formats facilitates network training to learn different types of features. Furthermore, it utilizes constraints from large and small tasks in the classification task, as well as other tasks, i.e., using different task objectives to achieve multiple constraints. This helps the network extract more general and comprehensive features, resulting in better classification performance for motor imagery EEG signals.

The invention provides a multi-feature fusion target trajectory prediction method based on a Transform model. The method comprises the following steps: step 1, inputting position information of a target trajectory into a feature generation module to generate multi-feature information such as speed, acceleration and angular speed; respectively embedding the generated multi-feature information and the original position information, then carrying out vector representation, and carrying out convolution fusion to form a multi-feature vector; step 2, transforming a multi-head attention mechanism of a Transform model, combining the extracted Fourier adaptive filtering and multi-head attention to propose a new Fourier adaptive filtering attention algorithm, modeling a dependency relationship of track sequence features by using the new algorithm, and adding convolution and pooling operations at the end of Encoder so as to further extract features; and step 3, the features processed by the Encoder for multiple times are sent to Decoder of the Transform model to be decoded, and prediction is carried out. The feature information of the target trajectory data can be efficiently mined and extracted, and high-precision trajectory prediction of the target is realized.

The present invention discloses a multimodal recommendation method and system for enhancing user representation with graph convolutional neural network. The present invention includes extracting project embedding, original user embedding and original ID embedding from the input data set, extracting features from the user's neighbors and performing aggregation operations to enhance the user representation to generate a user embedding after aggregating other user information, and then splicing it with the project embedding into a user-project collaborative interaction matrix, extracting features of each modality for the input data set and building a user-item graph of each modality in combination with the user-project collaborative interaction matrix; and then obtaining the user's recommended items through multimodal information representation, multimodal feature fusion and recommendation results. The present invention aims to improve the accuracy of recommendations by introducing multimodal information while introducing user feature enhancement technology, obtain the user's personalized embedding by aggregating the user features with the features of similar users, and finally improve the recommendation accuracy of the multimodal recommendation system.

The application belongs to the technical field of water treatment agents, and particularly relates to a multifunctional composite water purifying agent and a preparation method thereof, which comprises the following raw materials in parts by weight: multifunctional water purifying powder 90-100 parts, bentonite 8-10 parts, sodium carboxymethyl cellulose 0.5-1.5 parts, sodium percarbonate 0.5-1 part and sodium citrate 0.5-1 part; the multifunctional water purifying powder comprises core layer material and shell layer slurry; the multifunctional water purifying agent of the application takes the multifunctional water purifying powder with a core-shell structure as a core, cooperates with bentonite, sodium carboxymethyl cellulose, sodium percarbonate and sodium citrate, realizes efficient and synchronous removal of heavy metals, organic matters, colority and microorganisms, and has the advantages of wide spectrum, stability and easy recovery.

This application provides a surgical procedure recognition method, medium, and surgical robot. The method includes: acquiring multiple target video frames and multiple target kinematic data corresponding to each of the multiple target video frames, wherein the multiple target video frames include a current video frame and at least one historical video frame; the multiple target video frames include video frames of surgery performed using surgical instruments, and the kinematic data corresponding to each target video frame includes kinematic data of a robotic arm holding the surgical instruments when acquiring the target video frame; inputting the multiple target video frames and the multiple target kinematic data into a pre-trained surgical procedure recognition model, so that the surgical procedure recognition model can recognize the surgical procedure corresponding to the current video frame based on the multiple target video frames and the multiple target kinematic data.

The application provides a kind of based on improved Yolov5 water surface target detection method and device, it includes the following steps: 1) image acquisition, after data enhancement constructs water surface target set;2) using Gaussian filter to shallow feature map does smoothing operation and realizes image preprocessing;3) embedding multi-angle attention mechanism in Yolov5 network, so that the feature extracted is richer, improve the resolution decline, local information loss problem caused by ordinary method;4) feature scale conversion migration fusion Yolov5 network carries out feature processing;5) using Distance-IoU loss method loss calculation, finally, the three kinds of losses are weighted calculation, and then the network is optimized, and more accurate results are obtained.The method combines computer vision technology and detection technology and other fields, improves the efficiency and accuracy of water surface target detection, avoids the problems such as low detection accuracy, slow recognition speed and unsatisfactory multi-target detection caused by existing methods.

The application discloses a remote sensing image super-resolution reconstruction method, and relates to the technical field of image processing; and comprises the following steps: preparing a data set; constructing a network model; training the network model; fine-tuning the network model; and solidifying the network model.The technical scheme of the application designs a multi-dimensional interaction and conditional discrimination structure and training mode, introduces high and low dimensional feature interaction in the generator, promotes the generation of images with more abundant details, introduces gradient and semantic information in the discriminator, guides the generator to learn more fine-grained gradient and semantic perception texture, so that the generator can generate more realistic images, and the quality of the super-resolution reconstructed images is improved.

The invention relates to a small target traffic sign recognition method, a medium and computer equipment, and the method comprises the steps: constructing an improved recognition model with YOLOv11 as a basic model, and enabling the recognition model to comprise a small target detection layer; constructing a data set, training the improved recognition model, and recognizing the small target traffic sign by using the trained improved recognition model; the computer readable storage medium and the computer equipment are realized based on the method. According to the method, the shallow information of the small target traffic sign is better utilized, more small target traffic sign features are reserved, and the positioning precision of the small target traffic sign is improved; important feature channels are automatically concerned, the capturing capability of the network on key features is enhanced, and semantic information and detail features are fused more efficiently; the capturing capability of the model on the shape features of the feature pattern is improved, the calculation efficiency of feature extraction and fusion is optimized, and the feature utilization efficiency and the information richness are effectively improved; the cross-scale feature interaction capability of the model is effectively enhanced, and the detection capability of a detection head on a small target is improved.

This invention relates to a method for predicting the performance of hydrogen storage alloys based on feature engineering and multi-model screening, comprising the following steps: constructing a set of physicochemical features of the alloy; multi-method feature importance analysis; feature union extraction; feature screening based on importance-correlation joint analysis; feature subset search and multi-model cross-validation; and determination of the globally optimal model and the optimal feature subset. This invention provides a more comprehensive approach to feature construction and selection for subsequent machine learning research on hydrogen storage alloys; it can effectively identify features that are important in different methods, making the feature selection results more objective and reliable. Simultaneously, it eliminates multicollinearity among features through correlation redundancy removal and avoids the risk of local optima through full subset search, significantly improving the stability, reliability, and global optimality of feature screening; the prediction accuracy of this invention is improved by 90.8% and 55.9%, respectively.

This invention belongs to the field of collaborative drug prediction technology and relates to a collaborative drug prediction method based on kernel fusion of multi-source similarity matrices. Firstly, this invention overcomes the limitation of single-source information provided by drug structure, ATC encoding, and target features by introducing two types of information describing drug ADMET features: enzyme and pathway. Secondly, it uses a restarted random walk algorithm to process the similarity matrix and calculates the topological similarity between nodes under different similarity matrices using Spearman's rank correlation coefficient, overcoming the shortcomings of existing methods that do not consider the interaction information between different drug similarity networks, enabling the model to more comprehensively capture the multi-dimensional features of drug similarity. Finally, it uses a kernel fusion network to fuse the five topological similarity matrices, effectively nonlinearly integrating the topological similarity matrices of drugs under different conditions, overcoming the problem of existing methods simply merging all similarity matrices by averaging, which masks the differences in the importance of different drug features.

The application provides a positioning method and device applied to a ship environment. The method provided by the application comprises the following steps: collecting a group of positioning related data when a current sampling period arrives; determining a joint fluctuation index of an IQ sample according to a magnitude variation and a phase variation of the IQ sample, and determining an updated sampling period of next sampling according to the joint fluctuation index, a preset minimum sampling period and a preset standard sampling period; performing next round of data collection when the updated sampling period arrives, and repeating the above steps; screening and preprocessing a plurality of groups of continuously collected positioning related data to obtain a training sample set; training an initial positioning model based on the training sample set to obtain a trained positioning model; inputting a group of to-be-tested data collected in real time into the trained positioning model, so that the trained positioning model outputs a predicted position of a transmitter corresponding to real-time IQ samples in the group of to-be-tested data based on the group of to-be-tested data.

The application provides a digital radiograph image lead character recognition method and system, comprising: acquiring a digital radiograph image, and dividing the digital radiograph image into a training set and a test set; cropping the digital radiograph image in the training set to obtain a training set cropped image; manually labeling the training set cropped image; constructing a digital radiograph image lead character recognition network; constructing a loss function of the digital radiograph image lead character recognition network; training the digital radiograph image lead character recognition network to convergence using the training set cropped image; cropping the digital radiograph image in the test set, inputting the trained digital radiograph image lead character recognition network, and obtaining a recognition result. The application can obtain multi-granularity characteristics of lead characters, effectively increase the recognition accuracy of lead characters of different sizes, use a convolutional neural network to obtain image characteristics, and solve the problem of irregular placement of lead characters by using the rotation invariance of the characteristics extracted by the convolutional neural network.