40results about How to "Strong generalization" patented technology

The application relates to a kind of OFDM-based broadband millimeter wave relay system hybrid beamforming design method, comprising: calculating the receiving signal of target node under the condition that each node is all digital processor;The receiving signal is processed using minimum mean square error criterion in target node, and MMSE matrix is calculated;Using the equivalence between maximizing sum rate and minimum weighted mean square error algorithm, the maximum sum rate problem is converted into minimum weighted mean square error problem according to MMSE matrix;Deep unfolding neural network is used to solve the minimum weighted mean square error problem, and all digital processor of each node is obtained;Each node's all digital processor is decomposed based on least square decomposition algorithm, and hybrid beamforming matrix of each node is calculated, the complexity of the algorithm is greatly reduced, and the running time of the system can be effectively improved.

This invention discloses a method, system, device, and storage medium for predicting oil pipe corrosion rate based on a PCA-PSO-SVR hybrid model, specifically including the following steps: S1 Collecting corrosion detection data and operating condition parameters of the oil pipe to form a dataset; S2 Preprocessing the dataset; S3 Using the PCA model to perform dimensionality reduction on the dataset and extracting the main features affecting the corrosion rate; S4 Initializing the parameters of the PSO model; S5 Optimizing the parameters of the SVR model using the PSO model; S6 Constructing a corrosion rate prediction model based on the optimized SVR model; S7 Inputting the preprocessed dataset into the corrosion rate prediction model to obtain the prediction result and complete the prediction.

This invention provides a vehicle simulation speed correction method and system, relating to the field of vehicle intelligent dynamics modeling technology. The correction method includes the following steps: S1: Input and process simulation data output from the vehicle dynamics simulation model and corresponding real vehicle test data; S2: Construct a dynamic graph structure representing the interaction between state variables based on a preset physical coupling relationship of the vehicle powertrain; S3: Input the simulation data into a GCN and perform graph convolution operations under the constraints of the dynamic graph structure to extract graph embedding feature sequences representing the spatial dependencies between state variables; S4: Input the graph embedding feature sequences into a TCN and perform temporal convolution operations to learn the dynamic evolution law of state variables in the time dimension and output the correction amount of the vehicle simulation speed; S5: Output the result. Based on this, this invention solves the problem that existing correction methods have various limitations in practical applications.

The application belongs to the technical field of vehicle reliability evaluation, and discloses a vehicle component reliability evaluation method suitable for strong random working conditions. In the method, multiple random working condition parameters such as road surface grade and driving speed are uniformly represented as continuous variables, a single high-precision proxy model covering all working conditions is constructed, and advanced fast sampling algorithms (Latin hypercube sampling, mixed importance sampling, adaptive Markov chain Monte Carlo) and a segmented random task profile are combined to realize accurate and efficient evaluation of cumulative damage.

The application relates to the technical field of image processing, in particular to an image super-resolution reconstruction method based on a multi-scale content perception mixer, which is realized by using an adaptive processing mechanism. The method comprises the following steps: shallow feature extraction is performed on a low-resolution image to be reconstructed, so as to obtain an initial shallow feature map; feature enhancement based on a feature pyramid and an attention mechanism is performed on the shallow feature map, so as to obtain a deep feature map; multi-scale content perception prediction is performed based on the deep feature map, so as to generate guide information for guiding calculation allocation, the guide information comprising a window classification binary mask and a window size; different image regions are allocated to different calculation paths for processing based on the guide information; the feature maps output by the calculation paths are recombined and fused, and then enlarged to a target resolution, so that a high-resolution image is finally obtained. The method realizes accurate classification of image regions and on-demand allocation of calculation resources, and significantly reduces the calculation complexity and the memory occupation.

The present application relates to a kind of forest leaf instance segmentation method and system based on deep learning, method includes: obtaining vegetation image, vegetation image is input into leaf instance segmentation model, obtains leaf instance segmentation prediction result;Leaf instance segmentation model is trained using training set;Training set includes: vegetation original image;Feature extraction and enhancement are carried out using backbone module in leaf instance segmentation model, and adaptive spatial fusion mechanism in progressive feature pyramid network is integrated to dynamically adjust feature weight, generate dynamic fusion feature;Through the dynamic asymmetric spatial perception mechanism built-in in dynamic anomaly regression head module, the corresponding multi-source deformation feature layer of dynamic fusion feature is obtained, and the feature fusion strategy of top-down cascaded decoding module is used to optimize multi-scale feature, obtain multi-source fusion feature layer, further using multi-source fusion feature layer, generate leaf instance segmentation prediction result.The present application solves the problems of data scarcity, poor adaptability and low efficiency.

The application discloses a kind of for the method, device, equipment and medium of tab abnormality detection in battery cell production process, comprising: the tab area image collected in battery cell production process is segmented to generate tab mask;Based on the tab mask extraction multi-scale pixel-level feature vector, and the multi-scale pixel-level feature vector is input into unsupervised probability GMM model to identify pixel-level abnormal point;The pixel-level abnormal point is analyzed to obtain candidate abnormal area, and the parameter index of the candidate abnormal area is obtained;Determine whether the parameter index meets the set tab abnormality judgment rule, if meet, it is judged as tab abnormality, and tab abnormality result is output.Therefore, the application can be widely applied to the detection of tab folding, crease, fracture, misplacement and other abnormalities on battery cell production line.

This invention relates to the field of intelligent brain disease diagnosis technology, specifically providing an auxiliary diagnostic method and system based on multimodal decoupled dynamic graph learning. The method includes: acquiring and preprocessing multimodal data (such as neuroimaging, genetic markers, etc.) of the subject; extracting common pathological information and modality-specific features through a shared encoder and modality-specific encoders respectively, and optimizing the separation process using a decoupling loss function; furthermore, fusing all modality embeddings using a multi-head self-attention mechanism with a masked matrix to generate initial node representations, where the mask is used to suppress modality self-attention; subsequently, performing hierarchical dynamic graph convolution based on the node representations: in each layer, dynamically updating the graph adjacency matrix by combining the current node representation with the original features, and iteratively optimizing the node representations through message passing; finally, inputting the optimized representations into a classifier to obtain disease prediction results. This invention improves the automation performance and reliability of diagnosis.

A tool for compressor impeller and volute assembly relates to hoisting tool technical field. In order to solve the existing tool can only hoist one level impeller, there is weak universality, cannot satisfy the assembly demand of multilevel impeller. The utility model discloses a crossbeam, vertical beam, connecting piece and connecting disc, crossbeam horizontal setting, the top of crossbeam is connected with the lifting lug, and the lifting lug has the lifting hole, and the vertical beam is vertically installed at the bottom of crossbeam, and the vertical beam and crossbeam are welded connection, and the lifting lug and crossbeam are welded connection, and the connecting piece and connecting disc are detachably connected, the connecting piece is horizontally installed on the vertical beam and is located at the same side with crossbeam, the center hole of connecting disc can carry out the stop opening positioning to impeller, after positioning, first, connecting disc and impeller are fixed, then connecting disc is fixed on connecting piece, and hoisting hole can hoist impeller, and the assembly of multilevel impeller can be realized by replacing different connecting disc, and the generalization is strong.

The application belongs to the technical field of deep learning and remote sensing image processing, and specifically discloses a city land use mapping method based on multi-modal data collaborative perception, which comprises the following steps: performing parcel division on high-resolution remote sensing images of a research area to obtain irregular city parcels and remote sensing spectral features thereof; setting street sampling points along a road network, extracting street perception features and extracting interest point semantic features; constructing a heterogeneous graph structure based on the street sampling points, the street perception features and the interest point semantic features falling into the same parcel, and then extracting parcel-level multi-source geographic features; inputting the remote sensing spectral features and the parcel-level multi-source geographic features into a full sparse topic model for semantic alignment and fusion to generate fused parcel feature representations; and performing classification based on the fused parcel feature representations to output city land use mapping results. The application can realize high-precision and high-robustness city land use recognition under the condition that multi-source data is unevenly distributed or sparse.

The application provides a lightweight steel surface defect detection method and device and processing equipment, which are based on a YOLOv11 model, deep adaptability optimization is performed, the steel surface defect detection task has better detection precision, the parameter quantity and the calculation quantity can be effectively reduced, the generalization ability is good, when the industrial terminal is deployed, the high performance and the lightweight can be considered, and thus the industrial application prospect is good.

The application discloses an active defense method, device and equipment for diffusion speech conversion and a medium, and relates to the technical field of speech security. The active defense method comprises the following steps: obtaining source speech and reference speech, introducing a constrained protection disturbance into the reference speech, constructing protected speech, and ensuring that the protection disturbance satisfies a disturbance imperceptibility constraint. The source speech and the protected speech are input into a speech conversion model with the reference speech as a speaker condition, content information analysis, speaker condition constraint acoustic generation and waveform synthesis are completed by the speech conversion model, and a protection generated speech is output. A joint loss function is constructed based on speaker embedding representations of the protection generated speech and the reference speech, gradients of a current disturbance position and a predicted midpoint position are calculated according to the joint loss function and are fused to obtain an update direction, the protection disturbance is iteratively optimized, and projection or clipping is performed to satisfy the disturbance imperceptibility constraint. Finally, the protected speech is output.

The application discloses a centrifugal pump off-design condition diagnosis method and system based on a vibration signal, and the method comprises the following steps: collecting vibration signals of a centrifugal pump under multiple flow conditions, and constructing a vibration sample sequence with a working condition label; reconstructing the vibration signal by using a variational mode decomposition (VMD); performing a continuous wavelet transform (CWT) on the reconstructed signal, and mapping a one-dimensional vibration signal into a two-dimensional time-frequency graph; constructing a Light-CBAM-CNN diagnosis model, and training the same; inputting the vibration signal collected in real time into the trained diagnosis model after pretreatment, VMD and CWT, outputting a working condition category and a diagnosis confidence, and outputting an "uncertain" label and triggering a retest mechanism or an alarm when the confidence is lower than a threshold value. The application can realize online diagnosis of high-energy consumption off-design conditions of the centrifugal pump only by relying on the shell vibration signal, and has the advantages of small modification workload, low deployment cost, high identification reliability and strong energy-saving scheduling guiding significance.

The present application relates to a kind of small sample face recognition method and system, belong to face recognition technical field, solve the problem of the recognition result deviation of present face sample quantity short time being big. Including obtaining face picture, face picture is input to the face recognition network trained, obtain face feature vector, construct face image library;Face recognition network extracts the feature of public face dataset by training encoding network and decoding network, and the feature of large sample face dataset in it is migrated to the feature of small sample face dataset and is obtained by training fine-grained network;Real-time acquisition video picture is detected and preprocessed, obtain the face picture to be identified, input to the face recognition network trained, obtain the feature vector to be identified;Based on Euclidean distance, obtain the face picture corresponding to the face feature vector in face image library with the Euclidean distance minimum and less than threshold value of the feature vector to be identified, as recognition result. The accuracy of small sample face recognition is improved.

The application relates to an airplane static pressure source error correction method based on a Bayesian regularization neural network, comprising the following steps: constructing a mixed training data set, wherein the mixed training data set comprises flight test data, CFD simulation data and physical boundary data; pre-processing and dividing the mixed training data set to form a training set and a test set; constructing a Bayesian regularization neural network model and training; and using the trained Bayesian regularization neural network model to realize static pressure source error correction. The application uses CFD numerical simulation technology to economically and efficiently generate dense samples covering the whole design envelope, so as to make up for the insufficiency of flight test data in the coverage range and data density of the envelope; meanwhile, a Bayesian regularization training algorithm is introduced, the model complexity and data fitting degree are automatically balanced through Bayesian inference, overfitting is effectively inhibited, and the model generalization ability is improved.

This invention discloses a feature denoising distillation method guided by Logit information, belonging to the field of model compression technology. The method includes: first, encoding the discriminative direction of the teacher classifier using a Logit discriminative encoder to ensure feature alignment along the semantic direction; then, constructing a feature denoising mask to emphasize the teacher's target attention region while suppressing redundant background responses, thereby achieving spatial-level denoising in the feature space. Finally, a feature denoising distillation framework guided by Logit information is designed to achieve model compression. This invention provides explicit semantic direction guidance through a Logit discriminative encoder, suppresses background and noise activation through a feature denoising mask, and integrates both into the intermediate feature distillation process between the teacher and student models. It systematically solves the problems of lack of Logit semantic guidance, severe background noise interference, and insufficient focus of the distillation target in existing technologies, achieving high-precision and highly compatible model compression and knowledge transfer.

This invention discloses a multi-view few-sample Android malware classification method based on optimal guidance matching, belonging to the field of information security technology. The invention includes constructing multi-view grayscale images, training a backbone network, optimal guidance matching classification, and dynamic fusion of multiple views. First, the method extracts permissions, APIs, components, and intent features of the Android application to construct multi-view grayscale images. Then, it trains a backbone network that integrates attention mechanisms and self-supervised rotation prediction to extract discriminative features with geometric structure awareness. Valid guidance samples are identified through optimal guidance matching, and category similarity scores are calculated. Finally, adaptive weights are generated based on view confidence, and the multi-view scores are dynamically fused to complete the classification. This invention improves the accuracy and robustness of malware family classification in few-sample scenarios, reduces noise interference, and is suitable for rapid and accurate identification of malware.

The application provides a signal type recognition method based on reinforcement learning, and relates to the technical field of artificial intelligence.The method comprises the following steps: performing zero-mean normalization processing and slicing operation on IQ data of training signals to generate IQ sample data; constructing a data set with one-hot labels; constructing a deep learning strategy network and a deep learning target network; iteratively performing a training process: extracting a sample data from the data set as a current state; selecting an action according to an epsilon-greedy algorithm, executing the action, and obtaining a reward and a next state according to the corresponding relationship of the data set; updating the parameters of the deep learning strategy network by a back propagation method according to the loss value calculation result and an optimizer, and outputting a deep learning strategy network model; inputting a signal to be recognized into the trained deep learning strategy network, and outputting a final signal type by a voting algorithm.The method can realize efficient recognition of communication signal types, and has strong signal recognition accuracy and generalization ability.

The application relates to a SAR image water body submergence range change detection method and device for a flood scene. The method comprises the following steps: acquiring a training sample set containing pre-disaster and post-disaster SAR sample images and water body mask data, performing coherent speckle noise suppression and contrast enhancement preprocessing on the sample images, inputting the preprocessed images into a backbone double-branch twin network, extracting and interacting multi-scale features to generate double-time multi-scale feature maps, performing upsampling, splicing and channel space attention enhancement on the multi-scale feature fusion unit to obtain an optimized fusion feature map, generating a prediction result by a detection head, generating an intermediate heat map by a deep supervision unit, training a model to convergence by combining mask data to calculate a loss, inputting a to-be-detected image into the model after preprocessing to obtain a water body submergence range change detection result. The method can accurately extract the water body submergence range of a complex flood scene SAR image, effectively suppress noise, strengthen feature fusion, and improve the detection precision of the model to adapt to the real-time demand of flood emergency monitoring.

The application discloses a 6G wireless communication prediction channel modeling method based on large language model fine tuning, relates to the technical field of channel prediction, and comprises the following steps: processing channel measurement data, matching corresponding text data, dividing a training set and a test set, and constructing a channel prediction data set; designing a channel encoder and a dual-domain fusion module to extract channel features, and designing a text-driven encoder to extract text features; fine tuning a large language model by using the extracted channel and text features, enhancing multi-modal perception and transfer learning capability; designing a fine tuning module, fine tuning the output of the large language model, and projecting to predicted future channel state information; designing an angle consistency loss function, training a prediction algorithm based on large language model fine tuning in combination with prediction loss, and obtaining a trained network architecture; and iteratively predicting the space-time domain channel state by using the trained network, and outputting a channel prediction result. The system has high-precision prediction performance, and has outstanding practical value and popularization prospect.

This invention relates to the field of geological exploration, and in particular to a method and apparatus for correcting well logging acoustic velocity to VSP velocity trends. The method includes the following steps: acquiring well logging data and VSP data; preprocessing the well logging data and VSP data; determining the number of nodes in the input layer based on the feature count of the well logging data and VSP data; determining the number of hidden layers and the number of neurons in each layer based on the complexity of the problem and the characteristics of the well logging data and VSP data, thereby constructing a neural network model; training the neural network model; and inputting the well logging data to be corrected into the neural network to obtain the correction result. This invention improves the reliability of data support during modeling and imaging, and enhances the accuracy of modeling and imaging.

This invention relates to the field of interference source analysis technology, and provides a method and system for interference source analysis in partial discharge measurement of large oil-filled equipment, comprising: Step 1, acquiring the original acoustic signature signal of the large oil-filled equipment in operation; Step 2, preprocessing the original acoustic signature signal, including filtering, multi-stage downsampling, and acoustic signature data enhancement; Step 3, extracting the acoustic features of the preprocessed acoustic signature signal, including time-domain features, frequency-domain features, and time-frequency-domain features; and then performing multi-domain acoustic feature fusion; Step 4, based on the acoustic features, constructing a multi-domain feature-driven interference source classification model to identify and classify different types of interference sources; Step 5, based on the interference source identification results, suppressing interference in the original acoustic signature signal and extracting effective partial discharge signals. This invention can perform interference source analysis more effectively.

The present application relates to the field of image processing and computer vision, and provides a kind of based on GLSKF-tSVD's high missing color image restoration method, the method includes: obtaining the color image to be completed and its corresponding mask tensor, color image is represented as three-order tensor, define observation index set to construct corresponding projection operator and its complement projection operator, initialize the complete tensor to be recovered, global component and local residual component;Global-local additive completion model is constructed, t-SVD low rank structure based on t-product is used to parameterize representation for global component, structured covariance matrix is used to model for local residual component;Minimization objective function is constructed to solve model parameters;Alternating minimization strategy is used to solve minimization objective function, when meeting convergence condition or reaching maximum iteration number, output final completion tensor and restore to color image.The present application realizes the high-fidelity completion of color image under extreme high missing rate.

This invention discloses a method for generating human-pet interaction behavior based on multimodal semantics, relating to the fields of artificial intelligence, computer vision, and generative content processing. The method includes the following stages: Dataset construction stage: constructing source images in a subject-separated state and target ground truth images in a topic-deep interactive state, and establishing a twin alignment relationship between the two; Interaction command generation stage: setting global trigger commands as global keys to activate interaction capabilities; Model training stage: after configuring the training environment path, sequentially executing a two-order cross-dimensional semantic alignment strategy through network dimension configuration, training intensity and iteration control, and spatial dimension misalignment configuration, and using a dataset with twin alignment relationships for model training, enabling the trained model to generate target ground truth images from source images based on interaction commands. This invention can respond to commands to perform high-quality interactive editing of completely unfamiliar people and pets outside the training set.

The application discloses a signal modulation recognition method based on fusion of a graph neural network and a time-frequency network, and belongs to the technical field of signal processing and artificial intelligence. The method comprises the following steps: acquiring signal sample data sets, and pre-processing the signal sample data sets to finally generate a training set, a verification set and a test set; constructing a signal modulation recognition model; training the signal modulation recognition model based on the training set and the verification set; and testing the trained signal modulation recognition model based on the test set. The method simultaneously utilizes topological structure information between signal samples and time sequence dynamic information of the signal samples through a double-branch architecture, and realizes automatic extraction and deep adaptive fusion of multi-modal features through advanced modules such as a formulaically defined graph structure, a time sequence feature extraction structure and a bidirectional cross attention, so that the method exhibits excellent performance, strong robustness and good generalization capability in a signal modulation recognition task through end-to-end joint training and a precise optimization strategy.

The training method and estimation method of the crop key yield parameter integrated estimation network based on feature alignment belong to the cross technical field of agricultural remote sensing and artificial intelligence. In order to solve the problems of high cost, limited precision and insufficient feature utilization caused by the isolation of height estimation and segmentation tasks in the existing crop yield estimation method. The height estimation network and the target segmentation network are independently trained; then the verification set image is used for joint training, a single remote sensing image is input into the two pre-trained networks, the layer-in semantic aggregation degree of each layer in the two networks for the class is calculated, and then the semantic similarity is calculated, the layer with the maximum result is taken as the alignment layer, the feature alignment loss is calculated based on the semantic feature vector of the layer, and then the height estimation network loss and the target segmentation network loss are combined to complete the training of the model. Finally, the trained network is used for integrated estimation of crop key yield parameters.

This invention discloses a multimodal fusion crack detection and 3D modeling method and system for complex space unmanned aerial vehicles (UAVs), belonging to the field of intelligent inspection and structural health monitoring technology. The key technical points are: simultaneously acquiring RGB images, depth data, LiDAR point clouds, and GPS / IMU data using a multimodal sensor array mounted on the UAV; identifying and segmenting multiple types of damage, including cracks, based on an improved YOLOv11s-DySnake model; fusing multi-source data to calculate the 3D coordinates of the damage; constructing a structural model using an incremental 3D modeling algorithm and visually annotating the damage; and combining a hybrid A* algorithm with a dynamic obstacle avoidance algorithm to achieve autonomous inspection path planning. This invention achieves full automation from data acquisition to 3D modeling, improving the accuracy, robustness, and positioning accuracy of damage detection.

This invention relates to the fields of clinical laboratory medicine and artificial intelligence, and provides a risk prediction method and device based on machine learning for clinical mass spectrometry. The risk prediction method includes: defining N+M dimensions of features based on a liquid chromatography-tandem mass spectrometry system to obtain an N+M dimension feature vector structure; obtaining a virtual training dataset based on the N+M dimension feature vector structure, acquiring parameter values ​​of each dimension of the current batch through a data acquisition interface, and assembling them into N+M dimension feature vector data; performing format verification and invalid value filtering on the N+M dimension feature vector data using the N+M dimension feature vector structure to obtain a feature matrix; and obtaining a standardized real-time risk score based on the feature matrix and a trained fusion-integrated risk prediction model. This invention utilizes a machine learning model to uncover the complex nonlinear relationship between configuration parameters and dynamic parameters, thereby achieving more accurate and forward-looking risk warnings than traditional single-threshold methods.

The application provides a multi-modal automatic evaluation method and system for text-to-video, wherein the method comprises the following steps: constructing a Q-Save evaluation dataset, wherein the Q-Save evaluation dataset comprises a Prompt set containing dynamic elements, text-to-video, and labeled data, attribution text and MOS scores for each video; constructing an initial model based on a visual-linguistic large model, training the initial model by using the Q-Save evaluation dataset to obtain an evaluation model; taking a to-be-predicted text-to-video as an input of the evaluation model, and performing weighted average processing on the model output to obtain a final prediction score and attribution. The application can guarantee the video quality scoring capability and also provides reliable fine-grained attribution capability.