10results about How to "Mitigate Vanishing Gradients" patented technology

This invention discloses a method for monitoring the state trends of hydropower units based on two-stage signal decomposition and the IBiLSTM model. The method involves collecting vibration signals from the hydropower units for preprocessing; constructing the ITGCOA optimization algorithm and designing a fitness function; using the ITGCOA optimization algorithm to adaptively optimize the SVMD and BAACMD models, achieving initial decomposition of the preprocessed signal and secondary decomposition of the sub-mode component with the highest center frequency obtained from the initial decomposition; calculating the fuzzy entropy values ​​of the remaining sub-mode components for reconstruction; and fusing the high-frequency feature sub-sequences obtained from the secondary decomposition with the reconstructed feature sub-sequences to construct the input sequence of the prediction model. This input sequence is then used to construct the IBiLSTM prediction model for monitoring the state trends of hydropower units, achieving high-precision prediction. Compared with existing technologies, this invention improves the efficiency and accuracy of hydropower unit state trend prediction, accurately warns of abnormal unit operating conditions, ensures the safe and stable operation of the units, and improves the overall efficiency of the power plant.

The application belongs to the field of traffic flow prediction, and provides a city traffic flow prediction method, system and product under complex road network conditions, and the technical scheme is as follows: time mixing features are extracted based on city road network traffic flow time series data, intersection information between different traffic features is captured based on the time mixing features, mixed feature representation is obtained by fusing the time mixing features and the intersection information between different traffic features; global dependence in a long sequence is captured by selective state space modeling based on the mixed feature representation, and dynamic time series features are output; after the dynamic time series features are enhanced, a trailing time dimension is projected to obtain prediction values of each feature variable of the traffic time series data. In city traffic flow prediction, data can be dynamically processed according to real-time traffic conditions, multivariate information can be effectively utilized, and efficient long sequence modeling can be performed, and the prediction accuracy of time series data is significantly improved.

This invention belongs to the field of materials science, specifically relating to a crystal structure generation method based on variational autoencoders and Cartesian coordinate derivation. The aim is to construct a crystal structure design method driven by target properties. The method includes: acquiring real crystal structure data and performing data augmentation processing; training a variational autoencoder using the crystal structure dataset as the real sample and the target material properties as the input conditions; converting the real crystal structure data in the crystal structure dataset into crystal diagrams, performing feature derivation on the edges of the crystal diagrams based on the geometric information of atoms in the Cartesian coordinate system, and training a graph neural network based on the edge-derived features; inputting the target material properties into the fully trained variational autoencoder to generate candidate crystal structures; inputting the candidate crystal structures into the fully trained graph neural network for property prediction; comparing the predicted property values ​​with the target material properties within a preset target tolerance range and selecting the desired crystal structure.

This invention provides a method and system for identifying the lubrication state of self-lubricating spherical friction pairs, belonging to the field of mechanical equipment condition monitoring. The method collects acoustic emission signals during the operation of the friction pair using an acoustic emission sensor. After normalization, overlapping sliding window sampling, and data augmentation preprocessing, the signals are input into a dual-stream time-frequency fusion deep learning model. The model extracts complementary features through parallel time-domain and frequency-domain feature extraction modules, performs spatiotemporal modeling using a bidirectional GRU and Transformer encoder layer, and optimizes the model by combining a focus loss function and the AdamW optimization algorithm. Finally, the lubrication state prediction result is output. This invention fully utilizes the time-frequency characteristics of acoustic emission signals, achieving high accuracy, strong anti-interference capability, and good real-time performance. It can effectively identify the lubrication break-in period, stable period, and rapid deterioration period, providing a guarantee for the reliable operation of self-lubricating spherical friction pairs.

This application discloses a training method and related apparatus for a quantum generative adversarial network (GAN), belonging to the field of quantum computing technology. The GAN includes a generator and a discriminator. The method includes: using the generator to obtain generated samples against random noise; using the discriminator to distinguish between real samples and the generated samples, obtaining a discrimination result; updating the parameters of the generator and the discriminator based on the discrimination result, the loss function of the generator, and the loss function of the discriminator, to obtain a trained GAN. At least one of the generator and the discriminator includes a quantum convolutional layer and a quantum residual neural module connected sequentially. The quantum residual neural module includes a first qubit and a second qubit for encoding and evolving each feature data. Two qubits; a first quantum logic gate and a second quantum logic gate acting on the first qubit for performing an identity mapping operation on feature data; a third quantum logic gate acting on the second qubit and entangled with the first qubit between the first and second quantum logic gates, wherein the third quantum logic gate includes an encoding module for encoding feature data and parameterized training logic gates located before and after the encoding module for implementing residuals, and the encoding module includes an encoding logic gate and entanglement gates located before and after the encoding logic gate, the entanglement gates realizing the entanglement of the first and second qubits; the second qubit corresponding to one feature data at an adjacent position is the first qubit corresponding to another feature data, the feature data being obtained based on a quantum convolutional layer. Applying this application can effectively suppress the gradient vanishing problem and reduce the waste of training resources caused by training failures.

The application discloses a discharge detection device and method for a general high-low voltage switch cabinet for mines, and belongs to the technical field of switch cabinet detection. The device comprises a modularized sensor assembly, a signal acquisition unit, an edge computing node unit and an upper monitoring platform, and adopts a UHF sensor, an ultrasonic sensor and a transient ground voltage sensor for cooperative detection. The method comprises multi-source signal synchronous acquisition, adaptive denoising based on a sparrow search algorithm optimization, double-domain feature extraction, discharge type identification based on an attention-enhanced convolutional neural network, and multi-sensor information fusion and hierarchical early warning based on evidence theory. The application compensates for the blind area of a single detection method through cooperative work of multiple types of sensors, improves the denoising effect through adaptive parameter optimization, enhances the recognition accuracy through double-domain features and an attention mechanism, and improves the diagnosis reliability through multi-sensor fusion, so that accurate detection and intelligent diagnosis of local discharge of a mine switch cabinet are realized.

The present application relates to the field of artificial intelligence and data processing technology, specifically to a temperature control sensor data completion method based on a generative adversarial network, comprising obtaining a time series data matrix of a temperature control sensor containing missing data; constructing an adversarial model containing a generator and a discriminator; inputting the data matrix into a forward topological hard embedding layer, calculating the time and space partial differential physical residual and embedding the neuron transmission equation to output a latent feature matrix; inputting the latent matrix into an active feature selection module, eliminating redundant vectors and generating a synthetic sequence matrix; inputting the synthetic sequence and the real sequence into the discriminator to obtain the confidence, calculating the composite loss of the adversarial, reconstruction and residual constraints; using a gradient penalty mechanism to iterate the network parameters until convergence, and finally outputting the target temperature time series to complete the matrix. The present application effectively solves the problem of missing sensor data by introducing partial differential physical residual constraints and active feature selection, improving the adversarial network interpolation accuracy and the reliability of the time series sequence.

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.

This invention belongs to the field of character recognition technology and provides a handwritten letter recognition method based on piezoresistive signal detection, including the following steps: collecting the temporal piezoresistive signal during the letter writing process as raw data; preprocessing the raw data to obtain training data; constructing a recognition model, inputting the training data into the recognition model to extract temporal feature vectors, and performing classification training; using the trained recognition model for forward inference to confirm the letter category; this invention accurately captures changes in writing force and temporal logic based on piezoresistive electrical signals, significantly improving the accuracy of handwritten letter recognition.

The present application relates to the technical field of three-dimensional point cloud data processing and graph convolutional neural network, and particularly to a multi-domain adaptive graph convolution method and device based on visual selectivity. The present application takes the primate visual selectivity theory as the core basis, first constructs a multi-scale visual hypersphere neighborhood space, adaptively completes the polarization gridding of the neighborhood through clustering analysis, then designs a visual selectivity adaptive graph convolution operator to complete single-neighborhood feature extraction, constructs a deep network based on the visual hierarchical serial processing logic to realize high-level feature abstraction, and finally completes the point cloud classification task through multi-domain feature fusion. The present application solves the problems of information loss, topological structure damage, insufficient feature expression capability, and poor multi-scale feature adaptability in the existing 3D point cloud processing technology, and can be widely applied to 3D point cloud processing scenes in the fields of autonomous driving, augmented reality, robot perception, etc.