7results about How to "Strong feature extraction ability" patented technology

The application discloses a mechanical equipment fault diagnosis method, device and equipment and a readable storage medium. Known fault information multi-source frequency domain signals of same or same type mechanical equipment under a first working condition are acquired, and the known fault information multi-source frequency domain signals under the first working condition are taken as source domain data sets. Unknown fault information multi-source frequency domain signals of the mechanical equipment to be diagnosed under a second working condition are acquired, and the unknown fault information multi-source frequency domain signals under the second working condition are taken as target domain data sets. A multi-source dense adaptive adversarial network model is trained by using the source domain data sets and the target domain data sets, and a mechanical equipment fault diagnosis model is obtained. The real-time acquired multi-source frequency domain signals of the mechanical equipment to be diagnosed under the second working condition are input into the mechanical equipment fault diagnosis model, and a fault diagnosis result is output. The application can better utilize the multi-source information of the mechanical equipment in data level fusion and feature level fusion.

This application relates to the interdisciplinary field of deep learning and protein analysis, specifically to a method, apparatus, device, and medium for analyzing multi-target proteins in obesity based on deep learning. The method includes: acquiring biological sample data and preprocessing the biological sample data to obtain preprocessed biological sample data; the preprocessed biological sample data includes protein expression data and obesity classification labels corresponding to the protein expression data; the protein expression data includes multiple protein expression features; constructing an initial deep learning model for analyzing multi-target proteins in obesity based on a multilayer feedforward neural network; training the initial deep learning model based on the preprocessed biological sample data to obtain a deep learning model for analyzing multi-target proteins in obesity; and determining multiple protein expression features related to obesity based on the deep learning model for analyzing multi-target proteins in obesity.

This invention relates to the field of deep-sea signal processing and recognition technology, and in particular to a method for recognizing weak reverberation signals in the deep sea based on a deep learning model. The method includes: collecting reverberation data containing weak signals using underwater sensors; removing sensor noise and high-frequency interference from the reverberation data using a wavelet threshold denoising algorithm; normalizing the reverberation data to map its signal amplitude to a preset range; constructing a convolutional neural network model containing convolutional layers, pooling layers, and fully connected layers; dividing the normalized reverberation data into training, validation, and test sets; inputting the weak reverberation signal to be identified into the trained convolutional neural network model after denoising and normalization; and outputting the signal and recognition result from the convolutional neural network model. This invention eliminates a large amount of tedious manual feature engineering, reduces human intervention, and improves recognition efficiency and accuracy.

The application discloses a kind of HDR image reconstruction method, system, equipment and medium, it is related to computer vision image enhancement field, the method includes: obtaining the LDR image of different exposure degree under target scene;Each LDR image is mapped to HDR domain using gamma correction, obtain the gamma correction image of corresponding exposure degree;LDR image and gamma correction image set under target scene are input into image reconstruction model, obtain the HDR image of target scene;Image reconstruction model is obtained using training data to the deep learning model training;Deep learning model includes: feature extraction module and HDR reconstruction module;Feature extraction module is used to extract the double-channel feature containing channel information and spatial information to the input image;HDR reconstruction module is used to reconstruct the input image based on three-pass residual block according to double-channel feature, obtain corresponding HDR image.The application can realize end-to-end ghost-free high-quality HDR image reconstruction.

The present disclosure relates to a power distribution network service hot migration method and device, electronic equipment, storage medium and computer program product. The method comprises: obtaining resource information of each edge node in a plurality of edge nodes contained in a power distribution network and load information of services on each edge node; inputting the resource information of each edge node and the load information of the services on each edge node into a residual network contained in a trained service migration prediction model to obtain a feature representation; inputting the feature representation into a migration strategy decision module contained in the service migration prediction model to obtain a service migration instruction, wherein the service migration instruction is used to indicate a to-be-migrated service and a migration path and a migration destination edge node corresponding to the to-be-migrated service; and issuing the service migration instruction to an edge node containing the to-be-migrated service in the plurality of edge nodes, so that the edge node containing the to-be-migrated service performs hot migration on the to-be-migrated service based on the service migration instruction.

The invention provides a plateau railway icing early warning method and system based on sequence learning, and is applied to the technical field of meteorological disaster prediction, and the method comprises the steps: obtaining attention points and original meteorological observation data along a railway for several years, extracting the original meteorological observation data corresponding to the attention points, and obtaining meteorological data; preprocessing the meteorological data, generating an icing label corresponding to each concerned point location according to a preset icing standard, performing interval grouping based on longitude and latitude, and performing gridding processing on the meteorological data with the icing labels to obtain aggregated data; dividing the aggregated data into training data and test data according to a preset rule, and inputting the training data and the test data into a constructed space-time fusion deep learning model for rolling training to obtain a model parameter corresponding to each prediction year; and obtaining model parameters corresponding to the year to be predicted, substituting the model parameters into the model, inputting the meteorological data to be predicted into the model to obtain a corresponding icing risk prediction result, and performing early warning.

The invention provides a medical image map construction method and system, a storage medium and electronic equipment. The method comprises the following steps: acquiring medical images of a plurality of individuals; extracting high-dimensional features of the medical image based on an encoder; performing feature fusion on the high-dimensional features, and calculating a feature center of the high-dimensional features after feature fusion; and converting the feature center into a medical image map based on a decoder. According to the medical image atlas construction method and system, the storage medium and the electronic equipment, the efficiency of medical image atlas construction is effectively improved based on the deep learning algorithm, and the accuracy and applicability of the medical image atlas are enhanced.