5 results about "Linear embedding" patented technology

The application discloses a method, device, computer device and storage medium for training a model as a basis for a future time series prediction task. The method comprises: dividing past time series into a plurality of sequence blocks at a channel level, and linearly embedding the plurality of sequence blocks; adding a position embedding to the linear embedding of the plurality of sequence blocks; inputting the embedded plurality of sequence blocks into an encoder; and outputting a predicted future time series based on the encoder. In this way, more accurate prediction of future time series can be obtained under the condition of a limited number of past time series and zero-sample training, thereby providing greater inspiration for the field of time series prediction basis modeling.

The application belongs to the technical field of image processing, and discloses a chest and abdominal cavity bleeding automatic segmentation method and device based on deep learning, which comprises the following steps: obtaining and normalizing chest cavity or abdominal cavity CT volume data, and then performing linear embedding to generate initial features; inputting the initial features into an encoder for multi-stage encoding, in which the encoding features of different levels are respectively enhanced in the frequency domain according to different semantic levels to generate level-aware frequency domain enhanced features; the input features are modeled by a deformable mixed window multi-head self-attention mechanism in at least one stage of the encoder, and the attention calculation results are weighted and fused by using the frequency domain guide weight generated based on the frequency domain enhancement results; the encoding features are input into a decoder for multi-stage decoding and fusion with the corresponding level features of the encoder to gradually restore the spatial resolution; and the bleeding area segmentation results corresponding to the CT volume data are generated according to the final output features of the decoder. The application can efficiently and accurately segment the bleeding area and assist clinical decision-making.

The present disclosure provides a training method of an image extraction model and an image extraction method, which can be applied to the fields of image processing and pattern recognition. The image extraction model comprises a linear embedding network, an encoding network and a decoding network. The training method comprises: processing a sample image obtained by using the linear embedding network to obtain an embedding feature map; processing the embedding feature map by using the encoding network to obtain i encoding feature maps; processing the i encoding feature maps by using the decoding network to obtain a weight fusion feature map, wherein the decoding sub-network is constructed based on a local-global attention layer and a weight feature fusion layer; performing segmentation head mapping processing on the weight fusion feature map to obtain an image segmentation result, wherein the image segmentation result represents a geological change attribute of a target geographical environment region; and training the image extraction model according to the image segmentation result and label data corresponding to the image segmentation result to obtain a trained image extraction model.

The application belongs to the field of magnetic resonance sounding signal noise filtering, and is a kind of magnetic resonance sounding signal extraction method based on intelligent optimization manifold learning, the parameter group of the local linear embedding manifold learning method is initialized, the genetic algorithm in the intelligent optimization algorithm is used, the signal-to-noise ratio is taken as the fitness function, and the parameter group in the local linear embedding manifold learning method is optimized, the local linear embedding manifold learning method uses the optimized parameter group to sequentially perform first processing and second processing on the magnetic resonance sounding signal, removes random noise, and obtains the final denoised magnetic resonance sounding signal. The application effectively retains the signal characteristics through the nonlinear dimension reduction of manifold learning, avoids the information loss caused by frequency band selection, maintains the local relationship between data points in the dimension reduction process, ensures that adjacent points in m-dimensional space remain adjacent in d-dimensional space, only compresses and filters noise, and realizes signal extraction.

The present application relates to the technical field of power grid super-harmonic, and specifically discloses a power grid super-harmonic online monitoring and identification method and system, which utilizes a preset learning type measurement matrix to perform non-uniform compression sampling on original signals to be measured of a power grid, so as to obtain a low-dimensional observation vector sequence; the sequence is subjected to linear embedding processing and superposition position coding, so as to generate a feature vector containing time sequence information; the feature vector is input into a pre-trained Transformer reconstruction model, a high-dimensional time domain waveform after restoration is output through a decoding layer of the Transformer reconstruction model, so as to realize reconstruction of a super-harmonic signal; the reconstructed waveform is subjected to feature analysis, and monitoring parameters of the power grid super-harmonic or a harmonic source identification result is output. The present application can obtain a high-frequency signal under the condition of lower sampling rate of hardware, can reduce data transmission amount, and thus effectively reduces system cost; compared with a reconstruction algorithm of traditional compression sensing which depends on iterative solution, the present application only needs one-time forward calculation to complete signal reconstruction, and response speed is improved.