11results about How to "Reduce training difficulty" patented technology

The method belongs to the technical field of panoramic monitoring of ultra-high voltage converter station, and aims to solve the problem that panoramic monitoring image data is directly uploaded to the cloud, occupying a large amount of cloud resources. By adopting multi-scale convolution blocks in the deep multi-scale residual network model to construct low-order and high-order features of images of various scales, the incomplete phenomenon of image detail extraction is avoided, and the residual learning mechanism is adopted to retain low-order rough features, thereby improving the reconstruction ability of the image. Topology optimization constructs the topology structure of the heterogeneous network, and the framework combining deep reinforcement learning and Monte Carlo tree search is used to construct the network according to the pre-defined topology rules. The search result of the Monte Carlo tree strengthens the learning of the deep convolutional neural network, so as to obtain more accurate prediction in the next iteration. After the data is processed in the edge side, it is transmitted to the cloud storage, saving the cloud storage space and transmission bandwidth.

This invention discloses a method for predicting the viewport of panoramic video streaming with viewer type estimation. This method, tailored to different viewer types, trains a corresponding neural network prediction model for the viewport position using a set of viewport position vector samples. This makes the neural network prediction model more viewer-type specific, ensuring that a particular viewport position prediction model is only applicable to a specific viewer type, reducing the training difficulty of the model and improving prediction accuracy. This method determines the viewer type based on the viewer's actual viewport position sequence before the current frame and selects the corresponding neural network prediction model to predict the viewport position of the current frame. This method ensures that for different types of viewers, a corresponding trained neural network prediction model is used to predict the viewport position of the current frame.

The application discloses a kind of based on multi-source fusion and reinforcement learning's composite material additive manufacturing adaptive control method, first in printing process Synchronous acquisition space position, infrared temperature, tension and compression force and multi-source sensor data such as visible light image;Through the lightweight perception module constructed to each road data is parallelly processed and feature extraction, to force data adopts second-order difference to carry out state discrimination, to temperature data adopts multistage threshold segmentation and extracts forming area statistical feature, and reward feature is generated in combination with image recognition and position mutation detection;The multi-dimensional state features after fusion are combined with reward function, and the agent is trained using reinforcement learning algorithm to learn the optimal printing parameter decision strategy;The trained agent is used for real-time control, and the best printing speed and layer thickness instruction are dynamically output to the mechanical arm according to the current state. The application realizes accurate description and real-time adaptive control of complex multi-physical field coupling process, effectively improves printing quality and process stability.

The application discloses a multi-agent cooperative navigation optimization method, and the optimization method takes a multi-agent deep deterministic policy gradient algorithm as a basic framework; the following mechanism is introduced in the training stage: in the multi-agent cooperative navigation training stage, a joint priority experience extraction mechanism is introduced, joint experience samples are extracted and spliced according to the time difference error priority through cross-agent synchronization indexing; an information discarding and correction compensation mechanism is introduced at the input end of the centralized Critic network, information from other agents is divided into independent information blocks, the information blocks are randomly discarded with a preset probability, and a correction factor is applied to the retained information blocks to maintain the mathematical expectation of the input data unbiased, and the input of the reduced Critic network is obtained. The application provides a multi-agent cooperative navigation optimization method which can improve the key experience utilization rate, reduce the dependence of the model on high-dimensional global input, and enhance the robustness in a communication limited environment.

This invention proposes a method for denoising vibration signals from structural health monitoring based on a hybrid neural network. This method, implemented according to embodiments of the invention, eliminates the need for prior signal knowledge and manual parameter settings. It effectively removes various noise types from structural health monitoring vibration signals, significantly improves the signal-to-noise ratio, and reduces the root mean square error, achieving efficient and automated noise reduction of vibration signals.

This application provides a training method for a classification model, a text classification method, and related equipment to address the problem of low classification flexibility in classification models. The method includes at least the following steps: based on preset multiple feature dimensions, extracting corresponding multidimensional text features, multidimensional associated label features, and at least one multidimensional other label feature for selected sample text, associated classification labels, and at least one other classification label from among multiple classification labels; determining the negative sample similarity between each of the at least one other classification label feature and the multidimensional text feature based on the hierarchical distance between each of the at least one other classification label and the associated classification label; and adjusting model parameters based on the obtained positive sample similarity and at least one negative sample similarity between the multidimensional text feature and the multidimensional associated label feature. This enables a target classification model to have feature extraction capabilities of different granularities, improving classification flexibility.

This invention discloses an automated design method, device, computer equipment, and medium for corner bracing and anti-bracing structures within foundation pits, relating to the fields of smart structures and intelligent structural design. The method includes: constructing images for each stage of a three-stage design process for corner bracing and anti-bracing structures based on design images of these structures; semantically processing key structural information in each stage image to obtain semantically encoded images corresponding to each stage; decomposing the semantically encoded images of each stage into multiple single-bracing structure design images, and then combining and stitching these multiple single-bracing structure design images into a multi-bracing structure design image; and building a generative adversarial network (GAN) model, training the GAN model based on each single-bracing structure design image and each multi-bracing structure design image. This invention provides a fast, efficient, and highly visualized automated design scheme for corner bracing and anti-bracing structures within foundation pits.

This invention provides an image-assisted 3D object detection method, comprising the following steps: Step 1, extracting features from a point cloud containing the 3D object to be detected and its corresponding image; Step 2, fusing the features of the point cloud and the image based on a block self-attention mechanism to obtain fused multimodal features; Step 3, generating a foreground point mask based on the fused multimodal features and using it to extract multi-scale features with a focus on the foreground, obtaining feature maps of the point cloud at different resolutions; Step 4, generating an object detection proposal, i.e., interpolating the feature maps at different resolutions based on the three nearest neighbor interpolation method, fusing the interpolated features with the multimodal features using a multilayer perceptron, and feeding them into a two-stage detection module to generate an object detection proposal, thereby completing the image-assisted 3D object detection.

This invention relates to the field of fluid dynamics data reconstruction and intelligent computing technology, specifically a method and system for super-resolution reconstruction of vortex flow fields based on residual U-Net. The invention first performs bicubic downsampling on the high-resolution flow field and interpolates it back to its original size to construct a degenerate input. This input is then fed into a network containing an encoder, bottleneck layer, decoder, skip connections, and global residual connections. The output residual field is added to the input to obtain the reconstructed flow field. During training, a combined loss consisting of mean square error and gradient loss is used for supervised optimization. This scheme can effectively recover small-scale vortices, shear layer boundaries, and high-gradient textures in low-resolution flow fields, reduce over-smoothing, and improve reconstruction accuracy and physical structure realism. It is suitable for recovering flow field data after compressed transmission, enhancing low-precision measurements, and reconstructing numerical simulation results.

The application discloses a kind of gas pipeline leakage detection methods based on acoustic signal, comprising the following steps:S1, the leakage acoustic signal on pipeline is acquired and is handled;S2, the feature of the acoustic signal of the leakage acoustic signal after processing with gas pipeline normal operating condition is analyzed, and the characteristic quantity sensitive to gas pipeline leakage condition is established and extracted;S3, the characteristic quantity of other time-frequency domain of leakage acoustic signal is extracted, and the feature set is constructed, and the optimal characteristic quantity is selected based on feature selection algorithm, and the optimal characteristic quantity is used as the input of support vector machine, and the model is trained;S4, for the signal of real-time acquisition, by steps S1-S3 processing, the model of training is used to identify the state of gas pipeline.According to the application, the dependency problem of characteristic quantity to specific pipeline is effectively solved, a kind of robust gas pipeline leakage detection method is established, the accurate identification of gas pipeline leakage is realized, and the safe operation of city gas pipeline network is guaranteed.

The method for super-resolution reconstruction of panoramic monitoring image of protection system of extra-high voltage converter station belongs to the technical field of power equipment detection, and solves the problems of unclearness and low resolution of the existing panoramic monitoring image, which cannot meet the demand of panoramic monitoring of inspectors; by adopting multi-scale convolution blocks to construct low-order and high-order features of images of various scales in the deep multi-scale residual network model, the phenomenon of incomplete extraction of image details is avoided, the residual learning mechanism is adopted in the network model to retain low-order rough features, reduce the training difficulty, promote the reuse of features, and thus improve the reconstruction capability of the image; the reconstructed image has better structural similarity and peak signal-to-noise ratio performance; the standard data set and the panoramic monitoring image data set of the extra-high voltage converter station are used in sequence for image super-resolution reconstruction and target recognition experiment, and the experimental results show that the high-resolution image reconstructed by the method can meet the demand of panoramic monitoring of inspectors.