16results about How to "Fast training convergence" patented technology

This application discloses a method and apparatus for constructing a multi-scale adaptive gating MambaPlus network, belonging to the field of artificial intelligence and machine learning technology. The method includes: initializing the network configuration and constructing the basic structure; preprocessing the input data to generate a standard dataset; mapping the input data to the hidden space via an input mapping layer, and extracting backbone features from the Mamba backbone; constructing at least two parallel scale branches in the hidden space to obtain a multi-scale feature set; inputting the backbone features and multi-scale features into an adaptive gating module, dynamically allocating weights and adaptively fusing them through a hierarchical gating mechanism to generate fused features; further enhancing the features through cross-scale attention and feedforward enhancement, and then superimposing the residuals to generate the final discriminative features; finally, completing category prediction and model training evaluation. This application, while retaining the advantages of Mamba's long-range dependency modeling, addresses the problems of insufficient utilization of multi-scale information, poor adaptive feature fusion, and low robustness in complex scenarios.

The invention discloses a rolling bearing fault detection method and system based on mechanism and data fusion, and relates to the technical field of equipment state monitoring and fault diagnosis, and the method comprises the steps: collecting a multi-source operation state signal and an operation condition parameter of a rolling bearing in real time; constructing and solving oscillatory differential equations under different fault types based on the signals and the parameters in combination with a rolling bearing dynamical model, and generating a mechanism feature library; denoising vibration signals in the multi-source operation state signals, and performing data reconstruction on the denoised vibration signals based on a gamma hybrid model; generating a high-dimensional data feature set according to the reconstructed vibration signal, and performing dimension reduction on the high-dimensional data feature set by adopting a matrix t-SNE algorithm to obtain low-dimensional data features; constructing a joint feature set according to the low-dimensional data features and the mechanism features, and calculating a health index; and the severity and position of the fault are judged based on the joint feature set and the health index, so that the precise detection requirement of the early-stage tiny fault of the rolling bearing under the complex working condition can be met.

The present application relates to the technical field of computer vision, deep learning and graphics rendering, in particular to a 3D portrait reconstruction method and system based on cascade diffusion prior and UV space physical constraint, which adopts a parameter efficient adaptation paradigm of freezing a unified DiT backbone and a cascade LoRA adapter, and sequentially completes UV space texture completion, illumination homogenization and intrinsic material decomposition; introduces a cross-intrinsic attention mechanism to realize multi-material pixel-level spatial alignment, and executes differential physical rendering supervision to constrain material physical consistency in the UV domain. The present application realizes 100,000-level field image generalization under less than 100 3D scanning samples, and the generated 4K PBR material assets have no baking light and shadow residues, and the physical behavior is consistent under re-illumination, which is suitable for high-fidelity 3D portrait asset production in the fields of digital human creation, film special effects and virtual reality.

According to the method for training the target model based on reinforcement learning, the target model is used for executing tampering detection on an input image, and the method comprises the steps that a training sample is obtained, the training sample comprises a target image and boundary information of a plurality of truth value boxes, and the truth value boxes are borders of a part of images marked in the target image and comprising tampering content; and inputting the target image into the target model, and outputting boundary information of the plurality of prediction frames. And calculating the overall overlap ratio of the plurality of prediction frames and the plurality of true value frames. Determining a first reward score based on a preset piecewise function according to a numerical range of the overall overlap ratio; the function corresponding to each numerical range of the piecewise function is a constant function, and the value of the corresponding constant function is increased along with the improvement of the overall overlap ratio. And based on a reinforcement learning algorithm, according to the first reward score, updating the target model.

This invention discloses an AI-based low-code runtime anomaly identification system, comprising a data acquisition module, a node runtime anomaly identification module, a process runtime anomaly identification module, and a low-code runtime anomaly management module. This invention relates to the field of data processing technology, specifically to an AI-based low-code runtime anomaly identification system. This solution innovatively proposes performing full-dimensional anomaly prediction before low-code deployment and operation, and conducting three-level progressive runtime anomaly identification, thereby improving the stability of low-code process operation. It introduces TT tensor decomposition technology to construct a Laplace matrix, and uses a dynamic correlation matrix matching the current node component type as the correlation weight for graph convolution operations, improving the accuracy of the low-code node anomaly identification output. It employs a sine and cosine periodic perturbation chaotic mapping strategy and introduces a dual adaptive step-size mechanism to improve the optimization algorithm, thereby enhancing the stability and identification accuracy of the process runtime anomaly identification model.

The present application relates to the field of mobile robot autonomous navigation and artificial intelligence, and particularly relates to a mobile robot navigation method, which comprises the following steps: constructing an SCAG-TD3 deep reinforcement learning network integrating target guidance gating and residual target injection; establishing an automatic course learning system comprising bidirectional adaptive promotion and demotion and forgetting perception review; constructing a social control barrier function based on an anisotropic ellipse social domain, introducing a forward-looking point model to convert it into a linear inequality constraint, and forming a safety filtering layer; and encapsulating and deploying the trained policy network and the safety filtering layer. The present application combines deep reinforcement learning with social perception safety constraints, solves the problems of insufficient perception focus, low training efficiency and no guarantee of social safety in the prior art in a dynamic crowd environment, and realizes efficient, safe and socially compliant autonomous navigation of the robot.

The invention belongs to the field of coal gangue grading recognition, and particularly discloses a coal gangue grading recognition system based on deep learning, which comprises a coal gangue image acquisition module, a coal gangue edge separation module and a coal gangue classification module. According to the scheme, gray level inversion is carried out on the initial image to improve the detail definition, a bounded sum operator is applied to realize migration of low-brightness area pixels to a high-brightness area, adaptive threshold processing is adopted to screen edge pixels, and complete and accurate extraction of coal gangue edges is realized; the method comprises the following steps: extracting texture features by adopting empirical wavelet transform, fusing mean value, contrast and homogeneity features calculated by a gray-level co-occurrence matrix, building a deep convolutional pulse neural network fused with a pulse coding technology, introducing an arithmetic optimization algorithm to optimize key parameters of a coal gangue classification model, capturing spatial features and time sequence pulse features of a coal gangue image, and constructing a deep convolutional pulse neural network fused with a pulse coding technology; and comprehensive mining and efficient fusion of the multidimensional key features of the coal gangue image are realized.

This invention discloses a method and system for publishing multimodal data in industrial production for artificial intelligence, belonging to the field of industrial intelligence and data processing technology. The method includes: collecting multimodal data and performing hardware-level time synchronization and preprocessing to obtain a synchronized data stream; extracting multimodal features from the synchronized data stream in parallel and using an attention mechanism to fuse networks and generate a unified feature vector; constructing a five-dimensional quality assessment system and intelligently enhancing it; performing structured annotation and version management; adapting and publishing datasets according to AI tasks; implementing multi-level data anonymization and fine-grained access control, embedding invisible digital watermarks, and generating audit logs; and enabling multi-terminal access through a publishing platform. This invention solves the problems of difficult data alignment, poor quality, and low reusability, achieving secure data sharing and providing a high-quality, standardized data infrastructure for industrial AI applications.

The invention discloses a classroom facial expression recognition method based on an improved RT-DETR model. The method comprises the following steps: 1, obtaining a data set of classroom facial expression images; 2, preprocessing the data set by using a computer; 3, training a data set by using a classroom facial expression recognition detection model based on an improved RT-DETR model; 4, training the classroom facial expression recognition detection model to obtain an optimal detection model; and 5, performing real-time identification detection on the classroom facial expressions of the students in combination with monitoring and an optimal detection model. According to the invention, the CAFI module is designed in the backbone network, so that the classroom facial expression features can be extracted and screened more accurately; an MCFEEConv convolution feature enhancement module is designed in the head network, and the feature expression ability of faces with different distances and different sizes is improved; a RepC3LWGA module is designed in a head network, and joint modeling of local convolution features and global context features is achieved.

The invention relates to the technical field of electronic digital data processing, and discloses an industrial wastewater quality prediction method which comprises the following steps: acquiring historical time sequence data of key water quality parameters including an organic pollutant index, a nutritive salt index and a physical and chemical index, and preprocessing; performing periodic feature coding and linear trend feature coding on an input timestamp, and splicing all features into an enhanced feature sequence; inputting the enhanced feature sequence into a recurrent neural network for time sequence coding, and extracting a long-term dependency relationship of historical data; based on time sequence coding output, introducing an adaptive attention mechanism capable of learning a scaling factor to calculate the attention weight of each time step, and carrying out adaptive weighted fusion on the features; and processing the weighted and fused features, and outputting a plurality of key water quality parameter predicted values of a plurality of time steps in the future. The problems of insufficient utilization of time sequence features and weak multi-parameter correlation modeling are solved, and the purposes of multi-parameter joint prediction and adaptive attention adjustment are achieved.

The application relates to a progressive fine-tuning method and device for power scene model migration. The method comprises the following steps: obtaining a plurality of model parameters of a target model to be trained, determining the attention weights of each model parameter to each power knowledge entity in a pre-constructed power knowledge graph; mapping the attention weights corresponding to each model parameter into the activation scores of each model parameter; classifying each model parameter into a corresponding parameter layer according to the activation scores; obtaining a plurality of power corpora, and determining the first information entropy of each power corpus; classifying each power corpus into a corresponding corpus gradient according to the first information entropy; activating the parameter layer matched with each corpus gradient in the ascending order of the corpus gradient, training the activated parameter layer by using the power corpus of the corresponding corpus gradient, and obtaining the trained target model until the training stopping condition is reached. The method can improve the training efficiency and performance of model migration learning in the power scene.

The application discloses a soft-hard interbedded rock mechanical parameter prediction method and system based on a residual attention network, relates to the technical field of rock mechanical parameter prediction, and has the advantages that the traditional neural network is prone to gradient disappearance when processing a deep network, which influences the training effect; the existing method lacks an attention mechanism and cannot effectively identify and strengthen key features; and the modeling capability for interlayer interaction is insufficient; the application provides a soft-hard interbedded rock mechanical parameter prediction method based on a residual attention network, which comprises the following steps: obtaining structure parameters and target mechanical parameters of a soft-hard interbedded rock sample; converting the structure parameters into an enhanced feature vector; constructing a residual attention network model; inputting the enhanced feature vector into the residual attention network model; and outputting a mechanical parameter prediction result and reliability evaluation information.

The application discloses a flame and smoke identification method, and relates to the technical field of computer vision, and the method comprises the following steps: generating a boundary box of a flame and smoke target through a YOLOv target detection framework; extracting multi-scale visual features by using an image encoder of a SegmentAnything Model (SAM); encoding boundary box coordinates into a prompt embedding vector; fusing the visual features and the prompt embedding by using a mask decoder to generate a segmentation mask and a confidence score; confirming the target when the score exceeds a threshold value, and superimposing the boundary box and the segmentation mask and outputting to a fire-fighting early warning system.

The invention provides a tomato shelf life prediction method based on a hyperspectral and RGB imaging technology. The tomato shelf life prediction method comprises the following steps: S1, obtaining a hyperspectral image and an RGB image of a tomato sample; s2, preprocessing the hyperspectral image to obtain hyperspectral feature data; s3, extracting color feature data and texture feature data of the RGB image; s4, splicing the data obtained in the steps S2 and S3, performing standardization processing, and dividing the data into a training set, a test set and a verification set; s5, constructing an MLP model; screening the target feature set; s6, training the MLP model to obtain a tomato shelf life identification model; s6, obtaining a hyperspectral image and an RGB image of the detected tomato, performing preprocessing, and screening data according to the target feature set; and S7, splicing the screened color feature data, texture feature data and hyperspectral feature data of the detected tomatoes, inputting the spliced data into the tomato shelf life recognition model for recognition, and outputting the shelf life of the detected tomatoes. According to the invention, the accuracy of tomato shelf life identification is improved.

Embodiments of the present application provide a node selection strategy-based federated learning method, an electronic device and a storage medium, and relate to the technical field of federated learning. The method comprises: determining that a client node in a federated learning system will not be selected; obtaining an evaluation value corresponding to the client node, weighting the number of local training samples of the client node with the modulus of the evaluation value as the weight, and obtaining the selection probability of the client node according to the proportion of the weighted number of local training samples in the total sum of the weighted number of local training samples in the federated learning system; selecting a number of client nodes according to the selection probability of each client node to obtain a current round training set; and issuing model parameters to the client nodes in the current round training set. The embodiments provided by the present application reduce the overall training time of the federated learning model in the random node selection manner.

The present application relates to the technical field of robot motion control, and especially relates to a kind of quadruped robot reinforcement learning motion controller training, simulation, deployment method based on elevation map, the method constructs the asymmetric Actor-Critic deep reinforcement learning model based on double-estimator hybrid explicit and implicit feature state estimation, and the body state estimator and the terrain state estimator are respectively extracted body perception implicit feature and foot end terrain perception implicit feature;Build multiple simulation training terrains and design course teaching mechanism and multiple types of reward functions for training;Respectively build simulation verification environment based on MuJoCo and Gazebo, construct local elevation map by ray detection or laser radar point cloud fusion, and splice as the strategy network input with the body state of robot, the present application effectively improves the motion stability, environmental adaptability and simulation to reality migration reliability of quadruped robot in complex terrain.