6results about How to "Reduce computing needs" patented technology

The invention discloses an attention learning mechanism-based inverter model control prediction method, system and device, and a storage medium, and the method comprises the steps: pre-constructing a three-phase inverter simulation model, carrying out the real-time sampling of a state signal through employing an MPC controller, and obtaining a signal characteristic set; performing feature selection on the signal feature set, and extracting feature data; setting a screening threshold to screen the feature data, and taking the screened feature data as optimal features; inputting the optimal feature into a pre-trained machine learning model module to obtain a prediction result of the control signal of the three-phase inverter; the control performance can be improved while more calculation amount and communication traffic are reduced.

The invention discloses a drop-out fuse state monitoring method based on multi-size features and deep learning, and the method comprises the steps: S1, forming a data set for obtained drop-out fuse pictures, and dividing the data set into a training set and a test set; s2, adding an improved receptive field block and a coordinate attention module to a YOLOx backbone network, adding an adaptive spatial feature fusion module to PANet, carrying out secondary fusion on features of different scales, then introducing a loss function of weighting loss and positioning loss fusion, and finally carrying out lightweight improvement, constructing a state monitoring model, and carrying out state monitoring. Performing training verification on the constructed state monitoring model through the training set and the test set; s3, identifying a drop-out fuse picture acquired in real time by adopting the trained and verified state monitoring model; according to the image data collected by the application, the construction of a special database is realized, the YOLOx is improved and lightweight operation is carried out, and the complexity of the model is reduced, so that the method is suitable for an embedded platform of an electric unmanned aerial vehicle, and the detection effect of the drop-out fuse is improved.

The invention provides an image fusion method and device, and belongs to the technical field of image fusion, and the method comprises the steps: importing a plurality of original high-resolution hyperspectral images, and carrying out the down-sampling analysis of the original high-resolution hyperspectral images, and obtaining an original low-space hyperspectral image and an original high-space multispectral image; performing fusion analysis on the original low-space hyperspectral image and the original high-space multispectral image through a training model to obtain a target high-resolution hyperspectral image; and updating and analyzing the training model according to the target high-resolution hyperspectral image and the original high-resolution hyperspectral image to obtain an image fusion model. According to the method, the fidelity of the space and the spectrum of the fused image is kept, the calculation requirement is reduced while the spectrum and the space information are recovered, the calculation complexity is also reduced, better algorithm interpretability is achieved, and therefore a more accurate fusion result is obtained.

This invention discloses a method for constructing and predicting a relay health index based on Mahalanobis distance and multi-channel information fusion. The method includes: first, sampling the coil drive current signal sequence and contact voltage signal sequence during a single relay cycle, extracting the peak and trough time points and amplitudes of each sequence; then defining the first 10% of the relay's cycles as the healthy period, extracting the relative deviation between the current cycle feature value and the mean feature value of the healthy period, further calculating the Mahalanobis distance of the feature vector relative to the health status benchmark, and sequentially performing Box-Cox transformation, normalization, and monotonicity mapping to obtain the final health index. Using [variable name] as input and [variable name] as output, a random forest is selected as the regression prediction model for model training, and online prediction is achieved. The HI curve output by this invention avoids severe oscillations, greatly improving the stability of the prediction results and its engineering application value. Furthermore, the model is lightweight and suitable for edge deployment.

An intermediate set of sensor fusion is disclosed. Computer-implemented methods and related aspects for generating a predicted output for an automated driving system of a vehicle are disclosed. A computer-implemented method includes: obtaining a first sensor data set originating from a first sensor; a second set of sensor data originating from a second sensor different from the first sensor is obtained. Further, the computer-implemented method includes: processing the first sensor data set using the first set of encoder networks; a second set of sensor data is processed using a second set of encoder networks. The computer-implemented method further includes fusing the one or more first sets of encoded features with the one or more second sets of encoded features using a fusion algorithm; and generating a prediction output based on the set of fused encoded features using a decoder network trained to address perception tasks or planning tasks of the vehicle based on the encoded sensor data features.