12results about How to "Reduce data dimensionality" patented technology

This application belongs to the field of underwater acoustic array signal processing technology. This application provides a tensor-enhanced azimuth estimation method suitable for high-order acoustic field sensor arrays. The embodiments of this disclosure first construct a received tensor from the received data of the high-order acoustic field sensor array; then, multilinear low-rank modeling is performed on the received tensor, and the optimal first subspace factor matrix in the array dimension and the optimal second subspace factor matrix in the channel dimension are extracted using the HOOI method; next, the received tensor and the angle domain dictionary are synchronously projected into a low-dimensional tensor quantum space; finally, the sparse power spectrum in the angle domain is estimated using a multi-shot sparse Bayesian learning model, thereby obtaining the target azimuth. This method can suppress noise components in each dimension while preserving multidimensional coupling relationships, reducing the data dimensionality of subsequent sparse processing, reducing the influence of dictionary correlation and noise, and obtaining a sharper azimuth spectrum and higher resolution under conditions of low signal-to-noise ratio, low snapshot, and adjacent targets.

The application discloses a multi-modal radio frequency authentication method based on a multi-scale signal representation, which comprises the following steps: firstly, pre-processing the original IQ signal of a target device received to obtain an instantaneous envelope signal; carrying out multi-scale decomposition and denoising processing on the instantaneous envelope signal to obtain a denoised envelope signal; constructing a multi-modal data set according to the original IQ signal and the denoised envelope signal; carrying out feature extraction and fusion on the multi-modal data set through a pre-trained target feature extraction network to obtain a fused feature representation; and carrying out classification on the fused feature representation through a pre-trained target classification network to output a classification result corresponding to the target device. The amplitude, frequency and time-frequency energy distribution information can be complementarily fused by constructing the multi-modal data set; the multi-modal features are extracted and fused through the target feature extraction network, so that the information loss is effectively avoided; and the target classification network is used for rapid classification, thereby reducing the calculation cost and ensuring the accuracy of identification.

The invention relates to the technical field of ultra-precision machining, in particular to an ultra-precision cutting machining surface roughness online measuring method and system. First, a machining process signal is read. Secondly, calculating the weight of the processing process signal; the processing process signals are weighted according to the weights, and the weighted processing process signals are fused to serve as first input data; the machining process signal is used as additional input data. Then, obtaining a first input result and an additional input result according to the first input data and the additional input data, and obtaining a first fusion result according to the first input result and the additional input result; thirdly, inputting a high-dimensional feature fusion branch according to the first fusion result and additionally extracted first high-dimensional feature information and additional high-dimensional feature information to obtain a second fusion result; and finally, obtaining a surface roughness measurement result. According to the method, surface roughness prediction of ultra-precision cutting machining is more accurate.

The invention discloses an elevator intelligent maintenance method and device based on DGConv and an improved CBAM attention mechanism, and the method comprises the steps: firstly obtaining fault-related continuous data and discrete data based on an elevator mainboard, carrying out the data length alignment of the continuous data through dynamic time warping, and combining with the discrete data to construct an elevator fault prediction data set; then, constructing a CNN-Transform hybrid neural network of cavity global convolution and an improved CBAM attention mechanism for predicting the occurrence probability of various faults at the next moment, and performing training based on an elevator fault prediction data set; and finally, fault maintenance priorities are allocated to different faults according to the fault probabilities, and a targeted intelligent maintenance strategy is generated. The method provided by the invention has a remarkable effect in intelligent maintenance of the elevator, can greatly reduce the manpower and material resource overhead of a traditional maintenance method, saves the maintenance time, and optimizes the maintenance strategy.

The application relates to the technical field of accurate load prediction of a virtual power plant, and provides a load accurate prediction method and system for multi-source heterogeneous data fusion of a virtual power plant, so as to solve the problem that the existing technology is difficult to adapt to the accurate load prediction demand of a virtual power plant for a short period. The application collects photovoltaic output, wind power fluctuation data of a virtual power plant, and real-time current and node voltage monitoring data of a distribution network, associates and labels the former two to obtain an associated data set, phase-calibrates the latter two to obtain a calibrated data set, and integrates the two data sets into a multi-source heterogeneous data set; then, fluctuation characteristics of various data are extracted, an XGBoost algorithm is used to screen out a core information feature set affecting load prediction accuracy, and finally, a GRU time sequence prediction model is used to generate a load prediction result in a preset time period, so that the multi-source heterogeneous data of a virtual power plant can be fused, feature screening and time sequence correlation processing are performed, and accurate load prediction in a preset time period can be realized.

The invention discloses a haloxylon ammodendron forest biomass estimation method based on unmanned aerial vehicle multi-source data collaborative inversion, and relates to the technical field of environment remote sensing and information, and the method comprises the steps: obtaining a training sample set; obtaining unmanned aerial vehicle multispectral image data and unmanned aerial vehicle laser radar point cloud data of each training sample plot; extracting a first type of feature set comprising vegetation index features and texture features; extracting a second type of feature set comprising a height feature and a density feature; fusing the two types of feature sets to form a joint feature set; screening the joint feature set to construct a target feature subset; and carrying out model training by utilizing a machine learning algorithm to obtain an above-ground biomass estimation model. According to the method, the technical problem that the existing single remote sensing data source is low in precision in estimating the biomass on the desert sparse haloxylon ammodendron forest land is solved, high-precision and automatic estimation from the sample land to the regional scale is realized, and technical support is provided for accurate accounting of the desert carbon reserve in the arid region.

The application discloses a tobacco disease and pest monitoring method based on double-time-phase remote sensing features, comprising the following steps: S1, acquiring double-time-phase satellite remote sensing image data of a key growth period of tobacco; S2, extracting vegetation indexes, wavelet features and texture features from the double-time-phase satellite remote sensing image data respectively to form a single-time-phase feature set; S3, calculating double-time-phase features based on the single-time-phase feature set, wherein the double-time-phase features comprise feature difference features, relative change features and ratio features, and a high-dimensional feature set fusing the single-time-phase feature set and the double-time-phase features is constructed; and S4, performing feature screening on the high-dimensional feature set based on a recursive feature elimination algorithm to obtain a sensitive feature subset; the application has the beneficial effects that: by introducing the double-time-phase feature set, the application quantifies the change trend of the features in the key growth period, directly converts the dynamic development information of the diseases into identifiable model inputs, and enhances the distinguishing ability of the model to early diseases and different disease types and the early warning potential.

The application discloses a gait recognition method, system, device and medium based on point cloud, which comprises the following steps: performing point cloud projection on gait point cloud data to obtain a depth image sequence; encoding the depth image sequence to obtain initial features, inputting the initial features into a large-scale spatial attention module and a small-scale spatial attention module to extract multi-scale enhanced features, and performing feature fusion on the initial features and the multi-scale enhanced features to obtain first depth features; inputting the first depth features into a multi-scale space-time convolution network module to perform space dimension and time dimension feature extraction and fusion through a pseudo 3D convolution chain of several levels to obtain second depth features, and performing aggregation on the second depth features to obtain target depth features; and inputting real-time acquired gait data into a preset gait recognition model to determine a recognition object. The application can improve the accuracy and efficiency of gait recognition.

The application relates to a federated learning assisted edge caching method based on an AE-DDPM model, and comprises the following steps: an edge computing network system model is established, including a base station, a remote cloud server and users; an AE-DDPM model based on federated learning is used for training; global prediction content popularity is obtained, and the most popular N contents are cached according to the cache capacity of the base station; the application extracts a user data potential feature vector through an AE model, reduces data dimension and sparseness, and then learns data distribution through a DDPM model to generate high-quality content popularity prediction; the application deploys a cache unit on an edge node, enables users to quickly obtain the pre-cached contents on the node, effectively improves the cache hit rate, reduces the time delay of the users in obtaining the contents, significantly improves communication efficiency, and simultaneously reduces the risk of user privacy leakage.

The application discloses a feature automatic extraction method around a tooth boundary and a lesion recognition method, relates to the technical field of oral medicine, and comprises the following steps: acquiring a digital image of an oral X-ray apical film to be processed; performing automatic segmentation and contour extraction on a target tooth in the digital image of the oral X-ray apical film to obtain a tooth contour line of the target tooth; performing normalization processing on the tooth contour line; and traversing each boundary pixel point on the tooth contour line to generate an edge band feature map of the target tooth. The application can effectively extract local features highly related to lesions, especially features of a tooth boundary region, from the oral X-ray apical film, so that the recognition capability for early micro-lesions is improved.

This invention provides a method and system for online control of machining accuracy based on multi-sensor data fusion. The method includes the acquisition and preprocessing of a multimodal heterogeneous sensor dataset; weighted fusion using a weighted average method; extraction of aggregated feature vectors and time-frequency feature vectors using Local Feature Mean Aggregation (LFMA) and Wavelet Packet Transform (WPT); the introduction of a Fisher discriminant fusion algorithm to fuse the initial feature vectors; and prediction of machining error type and magnitude using an improved Extreme Learning Machine (ELM) model. Based on the error prediction results, an adaptive control strategy is constructed to control the machine tool online. This invention employs a multimodal data acquisition network using multiple types of sensors to acquire sensor parameters in real time, including dimensions, vibration, cutting force, temperature, and tool wear. Simultaneously, through a three-level hierarchical fusion architecture of data fusion, feature fusion, and decision fusion, the accuracy of error prediction is improved, thereby ensuring the accuracy of control.

The application discloses an elevator intelligent maintenance method and device based on DGConv and an improved CBAM attention mechanism, first obtains continuous data and discrete data related to faults based on an elevator mainboard, aligns the data length of the continuous data through dynamic time warping, and constructs an elevator fault prediction data set after merging with the discrete data; then constructs a CNN-Transformer hybrid neural network of a hollow global convolution and an improved CBAM attention mechanism, which is used for predicting the occurrence probability of various faults at the next moment, and is trained based on the elevator fault prediction data set; finally, different faults are allocated fault maintenance priorities according to the fault probability, and a targeted intelligent maintenance strategy is generated. The method disclosed by the application has remarkable effects in elevator intelligent maintenance, can greatly reduce the manpower and material resources consumption of the traditional maintenance method, save maintenance time, and optimize the maintenance strategy.