10results about How to "Solve vanishing gradient" patented technology

The application discloses a sea surface weak target detection method based on a residual network and a hypersphere constraint. The method can solve the problems of training underfitting or overfitting caused by the fact that the target sample is much smaller than the sea clutter sample under the sea clutter background, and the specific steps include: 1, performing short-time Fourier transform on each piece of data obtained after dividing the radar echo signal sample to obtain a time-frequency spectrum; 2, constructing an anomaly detection network model combined with a support vector data description based on a variational autoencoder of a residual network; 3, inputting the time-frequency spectrum into the anomaly detection network model for end-to-end training; 4, performing online detection of the target based on the trained model; and 5, evaluating the performance of the application from the constructed multi-dimensional index. The application can effectively enhance the separability between the target and the sea clutter, and improve the weak target detection precision under the condition of a real small sample.

The application provides a kind of based on feature enhancement and composite model's ultra-short-term wind power prediction method, belongs to ultra-short-term wind power prediction technical field, this method includes: by calculating the mutual information of multiple meteorological characteristics and wind power output and normalizing, screening to obtain key features, and constructing wind power prediction fitness function, by minimizing wind power prediction fitness function, obtain enhanced input features;By introducing one-dimensional convolutional neural network and bidirectional long short gate recurrent unit, construct the residual network of bidirectional long short gate recurrent unit and convolutional neural network fusion, and introduce space-time attention mechanism, construct to obtain composite residual network ultra-short-term wind power prediction model;And use multiple meteorological characteristics and enhanced input features to predict to obtain ultra-short-term wind power prediction result;The application solves the problem that the influence of meteorological multi-scale fluctuation on output cannot be accurately grasped, prediction is disconnected, disturbance is not considered and prediction accuracy continues to decline with step increase.

This invention provides a traffic flow speed prediction method and system based on directed hypergraphs and attention mechanisms, belonging to the field of traffic state prediction technology. Based on directed hypergraphs, it constructs the relationships between nodes in a road network; aggregates directed hyperedge information to characterize the complex spatiotemporal features of the road network; builds an encoder and decoder architecture; integrates an attention mechanism, constructs an attention module, and introduces an improved dense connection structure to enhance the method's accuracy. This invention effectively overcomes the shortcomings of methods based on traditional graph structures, achieves the extraction and fusion of temporal and spatial features, and solves problems such as gradient vanishing in deep neural networks. It is applicable to multi-step traffic flow prediction in real road networks and achieves good prediction accuracy.

This invention provides a method, apparatus, equipment, and medium for predicting oil and gas field flow data. The oil and gas field flow correction method includes the following steps: S1, acquiring and preprocessing wellhead data of oil and gas wells; S2, identifying influencing factors of wellhead flow data by analyzing the correlation between the wellhead data and wellhead flow data; S3, identifying influencing factors of wellhead flow data by analyzing the correlation between the wellhead data and wellhead flow data; S4, establishing a hybrid virtual metering model of oil and gas wells based on the aforementioned mechanism model, and labeling the characteristic variables of the hybrid virtual metering model according to the influencing factors; S5, predicting wellhead flow data based on the hybrid virtual metering model. This invention, by using existing data for mining and analysis, can achieve accurate measurement of wellhead flow, replacing the current complex back-matching mechanism, and can enhance the core competitiveness in the field of efficient oil and gas field development.

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.

The application provides a WGAN-based FRP sheet and concrete interface bonding slip model generation method, which can automatically, quickly and accurately obtain the bonding slip model and has a wide application prospect. After the automatic and rapid acquisition of the bonding slip model, the bonding performance between the FRP sheet and the concrete can be accurately reflected, and a safe and reliable reinforcement component design calculation method can be established. The WGAN is used to predict the strain, so that the training process can be simplified and the training can be stable. The WGAN can replace the traditional experimental analysis to quickly and accurately establish a bonding strength model. The LSTM is used as the generator of the WGAN model, so that the gradient disappearance and explosion problems of the RNN are solved, and the strain data related to time can be more accurately predicted. The CNN is used as the discriminator of the WGAN model, so that the quality and convergence speed of the generated samples are improved.

The invention provides a multi-site air quality prediction method based on adaptive hierarchical graph convolution, and belongs to the technical field of air quality prediction. The invention discloses a multi-site air quality prediction method based on self-adaptive hierarchical graph convolution, which is used for solving the problem that the real relationship between sites cannot be accurately represented by the current PM2.5 prediction method. According to the method, a hierarchical mapping graph convolution architecture is introduced, and different self-learning adjacency matrixes are used on different hierarchies, so that unique space-time dependence among different sites is effectively mined. And then connecting upper and lower adjacent matrixes based on an attention aggregation mechanism, and accelerating a convergence process. And finally, combining the hidden space state with a gating circulation unit to form a unified prediction architecture, capturing a multi-level space dependency relationship and a multi-level time dependency relationship, and providing a final prediction result. The invention provides a new thought and method for air quality prediction, and is helpful for better monitoring and controlling air pollution.

This invention relates to a method for diagnosing UAV motor faults based on dilated convolution residuals. The method comprises a data preprocessing module that converts one-dimensional vibration signals collected during UAV motor operation into two-dimensional grayscale images; a feature extraction module that extracts features from the input grayscale images to generate multi-channel feature maps; and a fault classification module that compresses the feature dimensions of the multi-channel feature maps, performs dimensionality mapping, and outputs the probabilities of various fault types, which constitute the fault diagnosis results. This invention improves fault identification accuracy by expanding the receptive field through dilated convolution and focusing on key features using a CBAM attention module, effectively capturing early, weak fault signals in the motor. The grayscale image conversion method in the data preprocessing stage does not require manual preset feature parameters, and the model can adaptively learn motor fault features under different operating conditions (such as different speeds and loads), with a generalization error of less than 5%.

This invention discloses an intelligent active fault-tolerance method for gradually changing faults in navigation sensors, comprising: constructing and training a standard signal prediction module using long short-term memory neurons as basic units to predict navigation signals; constructing and training a gradually changing fault diagnosis module using a multi-layer stacked convolutional neural network model, which extracts gradually changing fault trend information based on the residual between the predicted signal and the measured signal, and diagnoses the gradually changing fault in its early stages when its impact on the system is within a set range; if no gradually changing fault is diagnosed, the measured signal is used as the navigation signal; when a gradually changing fault is diagnosed, the predicted signal from the standard signal prediction module is used as the navigation signal, thus achieving active fault tolerance for gradually changing faults in the navigation signal. This invention can effectively predict lidar altitude information and solves the problems of slow gradually changing fault diagnosis and the limited application scenarios of traditional threshold methods.

The application relates to a brain tumor image region segmentation method and device, a neural network and electronic equipment, and comprises the following steps: acquiring a brain MRI image, forming a data set, and pre-processing the data set; an improved residual attention block is constructed, and a multilayer perceptron therein is replaced; an improved network model is constructed, the number of the improved residual attention block is adjusted, and a replacement method is replaced by introducing the improved residual attention block; the trained network model is input into a test set for testing, and the network effect is verified; the application has the beneficial effects that the application is based on a convolutional neural network, proposes a brain tumor segmentation method based on an improved residual attention mechanism of UNet, that is, a double-branch model structure, compensates for the problem that UNet cannot utilize global information of an image, and can effectively improve model segmentation quality.