9results about How to "Improve refactoring effect" patented technology

The application belongs to the technical field of bridge health monitoring, and particularly relates to a structure damage identification method based on a deep reconstruction network and multi-dimensional feature fusion, which comprises the following steps: obtaining an acceleration signal of a target bridge structure; inputting the acceleration signal into a Res-UNet-AE autoencoder to obtain a reconstructed signal, and calculating a reconstruction error and a signal-to-noise ratio according to the reconstructed signal; inputting the acceleration signal and the reconstructed signal into a perception autoencoder to obtain a perception index; fusing the reconstruction error, the signal-to-noise ratio and the perception index to obtain a three-dimensional damage feature space; and performing unsupervised clustering and identification according to the three-dimensional damage feature space to obtain a damage category identification result of the target bridge. The application can realize accurate differentiation of different working conditions of a structure without relying on any damage label.

The invention provides a topological interference power divider, a power control method and related equipment. The topological interference power divider comprises a dielectric substrate; the upper copper layer and the lower copper layer are arranged on the surface of the dielectric substrate; the valley photonic crystal structure is composed of triple symmetric apertures of periodic triangular lattices etched on the upper copper layer; the structure comprises A-type unit cells and B-type unit cells which are alternately arranged, the topological waveguide channel is a domain wall formed by the interface of the A-type unit cell and the B-type unit cell, and the channel comprises a plurality of branches which are intersected in a central area to form a coherent interference area; the input port and the output port are respectively arranged at the head end and the tail end of the topological waveguide channel; a matching network is arranged between each port and the channel and is used for realizing conversion between a conventional guided wave mode and a topological edge state; the modulation of the output power distribution ratio, the central working frequency or the transmission bandwidth of the topological interference power divider can be realized by regulating and controlling the topological edge state in the coherent interference region, and the flexibility and the reconstruction capability of power distribution are improved.

This invention discloses a signal enhancement method and apparatus for time-slice compressed sensing 3D imaging radar. First, an M×N dimensional random binary matrix is ​​generated as the measurement matrix for compressed sampling. The target area is illuminated using a laser source, and repeated sampling and modulation are performed to obtain a sampling matrix. The j-th column of the sampling matrix is ​​extracted to generate a compressed sampling vector for the corresponding time slice. The covariance between the compressed sampling vector and each column of the measurement matrix is ​​calculated to obtain the compressed sampling covariance vector. The covariance between each column of the measurement matrix and all columns is calculated to obtain the covariance measurement matrix. Based on a compressed sensing 2D reconstruction algorithm, the 2D image of each time slice is reconstructed using the covariance measurement matrix. The reconstructed 2D images of all time slices are stitched together to form a complete 3D image. This method and apparatus can solve the problems of low reconstruction accuracy and poor image quality in existing compressed sensing 3D imaging technologies under noisy environments.

The invention discloses a reconfigurable controller, an injection molding machine control system and an injection molding machine, which are applied to the field of injection molding machine control, and the controller comprises a first control unit, a second control unit and two network port modules, the first control unit outputs a first configuration signal when the controller is configured as a master station, and outputs a second configuration signal when the controller is configured as a slave station; according to the first configuration signal, the second control unit configures one network port module to adopt a preset upper communication protocol stack to perform bidirectional communication with the upper computer module, and configures the other network port module to adopt a preset master station communication protocol stack to perform bidirectional communication with the lower-level module; and according to the second configuration signal, the two network port modules are configured to adopt a preset slave station communication protocol stack so as to perform bidirectional communication with the superior module and the subordinate module respectively. According to the scheme, the controller is reliably and flexibly configured to be a master station or slave station role according to actual application requirements, the universality is high, and various architecture requirements such as centralized control and distributed control are supported.

The present application relates to image sensor, image processing technical field, for putting forward new image reconstruction method, obtains the image of removing motion blur, for this, the technical scheme that the present application adopts is, the image removing motion blur method for quantum image sensor, the block and image of input binary image sequence composition, patch-based optical flow algorithm realizes the alignment of block and image, based on the merging algorithm of incident photon number change with time realizes binary sequence merging, obtains the image of removing motion blur, wherein, the combination of multiple binary image sequences is called "block", the spatial window of pixel is called "patch", the sum of binary sequence in block is called "block and image". The present application is mainly applied to the design and manufacture occasion of quantum image sensor.

This invention belongs to the field of intelligent seismic exploration technology, specifically relating to an intelligent reconstruction method for passive source seismic wavefields under sparse active source constraints and non-ideal illumination, to address the problems of false phase axes and coherent noise under non-ideal distribution of underground passive sources. To improve the reconstruction effect of passive source seismic wavefields under non-ideal illumination conditions, this method constructs an improved U-Net network, enabling it to intelligently extract data features and derive attention mechanisms along two independent dimensions—channel and space—within the network. The attention mechanism is then multiplied by the input feature map for adaptive feature refinement, achieving intelligent reconstruction of passive source data. Furthermore, a deep learning network, trained through learning, constrains the passive source seismic data prediction network with sparse active source seismic records, enabling the reconstruction of passive source data with co-located active source lines under sparse active source conditions, thus improving the multi-dimensional deconvolution reconstruction effect and computational stability.

The application discloses a communication-efficient privacy-preserving personalized federated learning method. The application studies a personalized federated learning based on feature fusion mutual learning, which can realize communication-efficient personalized learning by interactive training of shared models, private models and fusion models on the client. Specifically, only the shared model is shared with the global model to reduce communication cost, while the private model can be personalized, and the fusion model can adaptively fuse local knowledge and global knowledge at different stages. Secondly, in order to further reduce the communication cost and enhance the privacy of the gradient, the application designs a privacy protection method based on gradient compression. The method can well realize privacy protection and lightweight compression by constructing a chaotic encryption circulant measurement matrix. In addition, the application also proposes an adaptive iterative hard threshold algorithm based on sparsity to improve flexibility and reconstruction performance.

This application relates to the field of intelligent transportation data processing technology, and in particular to a vehicle trajectory reconstruction method and system based on millimeter-wave radar. By fusing observation data from upstream and downstream millimeter-wave radars, the spatial blind spots between radars are considered as continuous missing trajectory regions. The missing parts are uniformly characterized in time to achieve standardization of the time dimension, thereby forming a vehicle trajectory reconstruction region. By comprehensively extracting the spatiotemporal interaction features of the target vehicle and surrounding vehicles within the vehicle trajectory reconstruction region, these features are used as input conditional feature sequences and fed into a model with group interaction modeling capabilities to generate the target vehicle's trajectory within the vehicle trajectory reconstruction region, achieving complete vehicle trajectory reconstruction. This aims to solve the problem of how to reconstruct vehicle trajectories in road segments with detection blind spots.

This invention provides a deep learning method for reconstructing sea surface current fields based on multi-source air-sea spatiotemporal features, relating to the field of marine data processing. Specifically, it includes: acquiring multi-year continuous sea surface current velocity data and multi-source environmental feature data for a target sea area; constructing a full-field training sample in a continuous time series manner, using multi-source environmental features from multiple consecutive historical moments as input to candidate deep learning models, and using the full-field data of the eastward and northward current velocity components at a future target time as prediction labels; constructing velocity moduli using the eastward and northward current components; training candidate deep learning models using a joint loss function; evaluating candidate deep learning models, selecting the optimal model, and using the optimal model to output the full-field current velocity results at the target time. The technical solution of this invention overcomes the problem in existing technologies of difficulty in obtaining long-term, large-scale, spatially continuous sea surface current field data.