9 results about "Denoising autoencoder" patented technology

The application discloses a CNN denoising method based on distance-Doppler information, comprising the following steps: acquiring continuous multiple frames of radar distance-Doppler images, and constructing a convolutional neural network model; introducing an effective loss solving function to the convolutional neural network model for training; inputting a current frame image, a previous frame image of the current frame and a previous two frame image of the current frame into the trained convolutional neural network model to obtain a denoised image of the current frame image, wherein the convolutional neural network model comprises a self-adaptive moving encoder module combined with a convolution block attention module, effectively avoiding the problems of redundant parameters and serious information loss caused by forward convolution and maximum pooling of a convolutional autoencoder and other convolution-based denoising autoencoders. Meanwhile, the feature map is adaptively encoded, and those feature maps containing target information are selectively emphasized, and those feature maps containing interference information are as much as possible ignored.

The application discloses a multi-source data processing method and system of a cigarette detection instrument, relates to the technical field of tobacco, and realizes non-discriminatory access to heterogeneous data sources through a unified interface protocol and automatic connection, avoids the huge workload of customized development and the problem of system rigidity, provides stable real-time data flow for subsequent processing, uniformly converts unstructured original data into standard structured records by using a formatted description template and automatic analysis technology, automatically and efficiently associates multi-instrument data of the same detection event by using a similarity matching algorithm based on local sensitive hashing, applies a weighting aggregation algorithm based on an attention mechanism to give differentiated weights to time series data and fusion, generates a fusion feature matrix that more comprehensively represents the quality state of a sample, performs automatic feature extraction and dimension reduction by using a stacked denoising autoencoder, and finally outputs results in the form of a standardized data packet with complete check information, thereby providing a ready-to-use unified high-quality data basis for upper-layer quality analysis and the like applications.

This invention provides a method and system for comprehensive performance evaluation of hot-rolled strip steel based on spatiotemporal topological representation, belonging to the field of intelligent monitoring of industrial processes. The method first acquires historical production time-series data and corresponding strip steel performance grade labels, and preprocesses and segments them to obtain local time sub-series; it constructs a twin denoising autoencoder network to extract slow feature representations, and then constructs a working condition state topology graph structure; it constructs a comprehensive performance evaluation model for hot-rolled strip steel based on graph convolution; it inputs the working condition state topology graph structure into the graph convolutional layer, and under the supervision of the strip steel performance grade labels, learns discriminative spatiotemporal features that integrate state dependencies along the time sequence dimension, then inputs them into a fully connected layer and a classifier to output the hot-rolled strip steel performance grade; it then evaluates the output results based on a loss function and optimizes the model parameters; finally, it performs a comprehensive evaluation of the hot-rolled strip steel performance based on the trained model. This invention improves the real-time performance and accuracy of comprehensive performance evaluation of hot-rolled strip steel.

This invention discloses a method and system for foreign object impact warning of power lines based on a spatiotemporal denoising autoencoder. The method includes: acquiring raw time-series signals from multiple sources and inputting them into a spatiotemporal denoising autoencoder; processing the raw time-series signals and reconstructing a reconstructed waveform of the line health state using a spatiotemporal mask; calculating the reconstruction residual between the raw time-series signals and the reconstructed waveform of the line health state, extracting current environmental background noise features and historical residual features, and inputting them together into a spatiotemporal risk tracker; using a multi-head attention mechanism and a convolutional gated recurrent unit to perform spatiotemporal dependency modeling on the input, and outputting multidimensional spatiotemporal features representing the current line operating state; calculating an adaptive dynamic judgment threshold based on the multidimensional spatiotemporal features to obtain a dynamic judgment threshold under the current operating condition; and performing a foreign object impact warning by comparing the offset between the dynamic judgment threshold and the reconstruction residual. This invention can significantly reduce the false alarm rate of warnings.

This invention discloses a method and system for foreign object impact warning of power lines based on a spatiotemporal denoising autoencoder. The method includes: acquiring raw time-series signals from multiple sources and inputting them into a spatiotemporal denoising autoencoder; processing the raw time-series signals and reconstructing a reconstructed waveform of the line health state using a spatiotemporal mask; calculating the reconstruction residual between the raw time-series signals and the reconstructed waveform of the line health state, extracting current environmental background noise features and historical residual features, and inputting them together into a spatiotemporal risk tracker; using a multi-head attention mechanism and a convolutional gated recurrent unit to perform spatiotemporal dependency modeling on the input, and outputting multidimensional spatiotemporal features representing the current line operating state; calculating an adaptive dynamic judgment threshold based on the multidimensional spatiotemporal features to obtain a dynamic judgment threshold under the current operating condition; and performing a foreign object impact warning by comparing the offset between the dynamic judgment threshold and the reconstruction residual. This invention can significantly reduce the false alarm rate of warnings.

This invention primarily addresses the performance issues caused by the limited computing power of wireless terminal devices in industrial control systems, as well as the high latency problems of traditional authentication methods. It proposes a physical layer authentication scheme based on ShuffleNet and a self-attention mechanism. First, a method based on an improved variational autoencoder and generative adversarial networks is proposed to augment CSI data, and a denoising autoencoder based on particle swarm optimization is used to reduce the dimensionality of the data. Then, a lightweight SE module is introduced as the implementation of the self-attention mechanism, further enhancing the model's focus on key features and improving its discriminative power and feature learning ability in complex scenarios. Finally, the ShuffleNet-SE model is used to identify CSI data features, classifying legitimate and illegitimate devices in the industrial control system.

This application discloses a method for detecting magnetic anomalies in background noise based on a sparse denoising autoencoder, belonging to the field of intelligent signal learning and perception technology. The method includes: acquiring noisy magnetic field data; inputting the data into a sparse denoising autoencoder for sparse denoising processing to obtain denoised magnetic field data; establishing a loss function; training the sparse denoising autoencoder using the loss function; acquiring magnetic field data to be detected; inputting the magnetic field data to be detected into the trained sparse denoising autoencoder to obtain the corresponding real-time reconstruction error; comparing the real-time reconstruction error with a set reconstruction error threshold to determine whether the magnetic field data to be detected contains magnetic anomaly signals. Compared with existing machine learning methods, the method in this application does not require manual annotation of massive amounts of data; it only requires training using unlabeled ocean magnetic field noise data collected in the ocean.

The application provides a bearing life prediction method and system based on a deep denoising autoencoder. It belongs to the field of bearing life prediction. The method comprises collecting vibration signals during bearing operation, extracting multiple time domain statistical features from the vibration signals to form an original feature set, normalizing the original feature set to obtain a normalized feature set, constructing a deep denoising autoencoder network model, using the normalized feature set as a training label, using an Adam optimizer to perform unsupervised training on the deep denoising autoencoder network model until the model converges, inputting the normalized feature set into the trained deep denoising autoencoder to obtain the output value of a single neuron hidden layer as a single-dimensional bearing degradation index generated by fusing all input time domain features, and outputting the remaining service life of the bearing based on the change of the single-dimensional bearing degradation index over time. The method can improve the prediction accuracy of the remaining service life of the bearing.

A recommendation method and system based on a hybrid denoising autoencoder, by inputting user-item rating data into a pre-trained generative adversarial network to complete the missing values in the user-item rating data, for each user, the rating values of each item are sorted in descending order to generate a recommendation list. The autoencoder is used as the generator of the generative adversarial network, and the original user-item rating data is added with noise during training, and the user-item rating data with noise is used for training, which improves the robustness of the generative adversarial network. The hybrid use of the autoencoder and the generative adversarial network can achieve complementary advantages and fully exert their respective advantages, learn deeper features of the rating data, and the generated adversarial network obtained after training can fully learn the difference between noisy and denoised data, complete the user-item rating data, and alleviate the data sparsity problem.