33results about How to "Preserve feature information" patented technology

The invention discloses a denoising method for a partial discharge ultrahigh-frequency signal of a transformer based on the improved variational mode and SVD (singular value decomposition). The methodcomprises the following steps: collecting a partial discharge signal of oil-paper insulation of the transformer by using an ultra-high frequency detection method, performing the variational mode decomposition of the collected partial discharge signal; optimizing the parameters of the variational mode decomposition algorithm through using the evolutionary algorithm, and introducing a kurtosis indicator to filter out narrowband noise; performing the SVD of a signal with the narrow-band noise being filtered; improving a singular value screening process in the SVD algorithm through using a clustering algorithm, thereby achieving the better filtering of white noise. The method has the characteristics of high precision, good denoising effect and small distortion, and is suitable for real-time monitoring of oil-paper insulation of the transformer, partial discharge signal detection and the like.

The invention discloses a method of automatically analyzing a 3D point cloud registering error, based on stereo grids. The method of automatically analyzing a 3D point cloud registering error, based on stereo grids includes the following steps: 1) obtaining a source point cloud; 2) obtaining a transformation matrix; 3) performing down-sampling on the source point cloud; 4) filtering the source point cloud; 5) obtaining the target point cloud; 6) extracting the characteristic points; 7) calculating the characteristic vector of the characteristic points; 8) looking for the corresponding points between the source point cloud and the target point cloud; 9) calculating the average distance between the corresponding points; 10) when the number of the discovered corresponding points is reduced to be less than the threshold, entering the step 11, or modifying the control variable looking for the corresponding points, and returning to the step 8; and 11) through weighted average, obtaining the error of the laser radar 3D point cloud registering. The method of automatically analyzing a 3D point cloud registering error, based on stereo grids can quantitatively compare the accuracy of using different registering algorithms to construct a 3D scene model, can help the registering algorithm to determine the suitable point cloud scale in practical application, and can be used to the laser radar three dimensional plotting and structural measurement field with higher requirement for registering accuracy.

The invention provides a hyperspectral remote sensing image classification method based on the attention mechanism and convolutional neural network, which performs principal component analysis on the original hyperspectral remote sensing image to reduce the dimensionality, and samples and blocks the hyperspectral data after dimensionality reduction; then performs 3D Convolution operation and pooling operation to obtain the intermediate feature map; then each spectral vector of the intermediate feature is multiplied by the spectral attention module and each spatial feature and the spatial attention module respectively to obtain the attention enhancement sample; Then perform another convolution operation and attention enhancement operation; then input the intermediate feature map obtained through the 3D convolution operation into the classifier for classification. The invention has the beneficial effects of reducing the classification cost, improving the classification performance, realizing adaptive feature refinement through the extraction and enhancement of sample features, and further improving the classification accuracy of hyperspectral remote sensing images.