9results about How to "Shorten training time" patented technology

The application provides a wall-penetrating target behavior recognition method based on channel state information, which comprises the following steps: preprocessing the collected CSI signals; performing PCA data dimension reduction on the preprocessed CSI signals, removing redundant signals and irrelevant information, and extracting optimal subcarriers; performing first-order difference processing on the signals extracted after PCA dimension reduction, and then using a method based on a buffer sliding window to segment effective feature signal segments; converting the effective feature signal segments into feature images with time-frequency domain features through STFT, and inputting the feature images into a pre-trained SE-ResNet18 convolutional neural network for behavior recognition classification. The method can penetrate the wall to realize the behavior recognition of the human target behind the wall. Compared with the traditional deep learning network, the method adopts a small sample transfer learning method combined with a pre-trained model, and the recognition accuracy can reach 91.67% under the conditions of fewer training times, fewer iteration times and shorter training time, so that the behavior recognition classification task can be effectively completed.

The present application relates to the field of signal detection, and particularly to a non-cooperative unmanned aerial vehicle burst frequency point detection method and device based on a Seg-Yolo model. The Seg-Yolo model proposed by the present application does not require prior sequences of non-cooperative unmanned aerial vehicle signals at all, and does not require prediction of specific sequences, and can directly realize continuous, discontinuous, overlapping and non-overlapping signal frequency point detection in a time-frequency graph. Through preprocessing, image noise reduction and the Seg-Yolo model, the present application reduces the image volume that needs to be processed and simplifies the size of the deep learning network, can accurately lock the center frequency of each frequency point on the basis of reducing the training time and improving the training efficiency, and improves the accuracy and reliability of signal extraction based on complex frequency hopping signals.

The application belongs to the technical field of computers and discloses a parallel segmentation learning method and device based on clusters, equipment and a storage medium. The method comprises the following steps: obtaining a plurality of to-be-learned user terminals and user communication information of each to-be-learned user terminal; performing cluster division on each to-be-learned user terminal according to each to-be-learned user terminal and the user communication information of each to-be-learned user terminal, and determining a plurality of target user clusters; and performing cluster serial segmentation learning according to an aggregated user terminal model and each target user cluster to obtain a target user terminal model, wherein the aggregated user terminal model is obtained by performing parallel segmentation learning on each target user cluster according to the target spectrum resources of each to-be-learned user terminal. Through the above method, the overall training time delay in the segmentation learning process is effectively reduced, the efficiency of segmentation learning is improved, the negative effects caused by network heterogeneity and dynamics are inhibited, and the convergence and accuracy of existing segmentation learning technology are ensured.

The invention discloses an optical receiver construction method, device and equipment, a communication system and a storage medium, and relates to the field of network communication, and the method comprises the steps: constructing a first neural network equalizer of a first communication link based on a neural network; configuring a model parameter of a second neural network equalizer as an initial model parameter of the first neural network equalizer; based on the initial model parameter, performing fine tuning training on the first neural network equalizer by using training data of the first communication link to obtain a target neural network equalizer; the target neural network equalizer is suitable for the optical receiver of the first communication link; the first communication link is different from the second communication link. Through migration and multiplexing of knowledge, the construction process of the optical receiver is converted from repetitive low-efficiency labor to efficient adaptive learning, so that the deployment efficiency and expandability of an optical fiber communication system are greatly improved.

The application discloses a kind of based on deep learning's extremely weak light environment under moving target scattering imaging method and device, it is related to extremely weak light environment under moving target imaging field, for the speckle image obtained by detector, establish neural network to obtain position information and original image recovery information.The accurate position information of target is obtained using prior information and recovered speckle to carry out trajectory recovery.N fixed positions in the detection range are placed target, and the sampling graph of fixed position for training is obtained.N fixed positions of sample speckle are used to train neural network, and after training, fixed parameter becomes the neural network for low-light imaging position classification.Each fixed position of sample speckle is used to train neural network respectively, and after training, fixed parameter becomes the neural network for low-light imaging reconstruction in fixed position.The application uses multiple position neural networks to recover speckle together, obtains more environmental information, and is conducive to generating the accuracy of detection imaging under the complex motion state of target.

The application relates to a robot autonomous navigation method based on local information, which comprises the following steps: step 1, a virtual navigation platform is built, an autonomous navigation model is designed, and training data of a virtual navigation process are collected; step 2, based on the training data, a deep reinforcement learning algorithm is used to train the autonomous navigation model; step 3, the trained autonomous navigation model is derived and encapsulated; and step 4, the encapsulated autonomous navigation model is applied to an actual navigation task. Compared with the prior art, the application has the advantages of high scene applicability and low calculation complexity.

The application discloses a three-dimensional model light ray tracing method based on meta learning, and belongs to the technical field of three-dimensional model image processing, and comprises the following steps: step one, constructing a general model for light ray tracing; step two, training a previous model by using a stereo matching convolutional neural network; step three, jointly training the previous model to obtain a new model; and step four, finding customized parameters and a customized model of the new model meeting a specific light ray tracing effect.The application trains an old model by using a stereo matching convolutional neural network based on the construction of a general model, trains general parameters meeting a specific light tracing effect, obtains new potential general parameters through the convergence of a loss function, adapts to a specific light tracing effect, effectively trains the old model by removing 90% of previous data and 98% of previous data, reduces the training data capacity, improves the accuracy of new and old data, and thus solves the problems of long training time, large training data capacity and low light ray tracing effect accuracy of the light ray tracing technology.

The application discloses a panoramic camera auxiliary-based GNSS non-line-of-sight signal identification and application method, constructs a light MN-DLV3-CRF model to optimize the boundary information, obtains a clear segmentation boundary, and constructs a sky map with shielding information; the sky map is divided into at least a non-shielding sky area, a building shielding area and a tree shielding area according to the segmentation boundary; when a satellite signal falls into the building shielding area and the tree shielding area, the satellite signal is determined as an NLOS signal; if the satellite signal falls into the tree shielding area, the NLOS weight of the tree is calculated by considering the NLOS weight model of the tree. The method generates a shielding sky map by semantic segmentation of a sky image, assists a GNSS receiver in identifying NLOS signals shielded by buildings, trees and the like, improves positioning precision in complex scenes such as urban canyons and shaded roads, and is suitable for high-precision positioning scenes such as vehicle navigation, unmanned aerial vehicle positioning and intelligent transportation systems.

Embodiments of the present application provide a metasurface unit spectrum prediction method, device and equipment and a storage medium, belonging to the technical field of metasurfaces. The method comprises: obtaining at least two target metasurface unit structure image sets; wherein the number of sampling points of images of different image sets is different; training a preset original spectrum recognition model based on each image set in turn according to the order of the number of sampling points from small to large to obtain a target spectrum recognition model; obtaining a to-be-predicted metasurface unit structure image, inputting the to-be-predicted metasurface unit structure image into the target spectrum recognition model for prediction, and outputting a target transmission coefficient; and performing inversion calculation on the target transmission coefficient to obtain an amplitude spectrum and a phase spectrum of the to-be-predicted metasurface unit structure image. By using the progressive training mode, the number of training images is reduced, the training time is shortened, and the target spectrum recognition model trained has a short prediction time and a high prediction result accuracy.