5results about How to "Avoid time overhead" patented technology

The invention discloses a GCN-based low-orbit Internet routing mechanism SDC vulnerability prediction method, and the method specifically comprises the steps: analyzing parts, which are liable to be affected by SDC, in a communication mechanism of a target network, and constructing a fault model facing a low-orbit Internet routing mechanism and causing a soft error by a single event effect; aiming at an embedded program in a target network communication mechanism, extracting the characteristics of an instruction in the program, and evaluating the SDC occurrence probability of the instruction; constructing an instruction SDC vulnerability prediction model ISVPG based on GCN, constructing an instruction dependency graph according to the extracted instruction features and the dependency relationship, and training an instruction SDC vulnerability prediction model based on a graph convolutional neural network; and according to the characteristics of other instruction nodes in the training sample graph, performing prediction by using a GCN-based instruction SDC vulnerability prediction model, and identifying the instruction SDC vulnerability. According to the method, the space-time overhead of prediction can be reduced, the prediction accuracy is improved, guidance is provided for a radiation hardening scheme of a low-orbit internet routing mechanism, and the reliability of the network routing mechanism is improved.

The invention discloses a rapid magnetic resonance imaging reconstruction and segmentation method and system, a medium and a product, and relates to the technical field of image processing. The method comprises the following steps: decomposing original three-dimensional magnetic resonance data into a plurality of independent two-dimensional magnetic resonance data slices along a slice sequence; image reconstruction is carried out on the two-dimensional magnetic resonance data slices through a pre-trained forward generator in the frequency domain Schrodinger bridge diffusion model so as to generate a two-dimensional magnetic resonance image, and the forward generator integrates a Fourier convolution module and is used for carrying out frequency domain transformation and feature enhancement processing on feature mapping in the image reconstruction process; inputting each two-dimensional magnetic resonance image into a pre-trained segmentation model in sequence, performing segmentation of a target region and a preset dangerous organ, and generating a corresponding two-dimensional segmentation mask; all the two-dimensional segmentation masks are rearranged and stacked according to the slicing sequence, and a three-dimensional segmentation result is generated through three-dimensional fusion operation. By implementing the technical scheme, magnetic resonance imaging reconstruction and segmentation can be quickly and accurately carried out.

The invention discloses a composition understanding method and system based on text inversion. According to the method, an input image is converted into a pseudo-token by adopting an optimized text inversion algorithm, an original text and the pseudo-token are spliced, the spliced text is converted into a feature vector, and the similarity between the text and the image is calculated. According to the method, the understanding ability of the composition understanding model is improved under the condition of reducing dependence on hard negative samples, the model interaction efficiency is improved, and meanwhile time consumption is reduced.

The application discloses a CPU performance index prediction method based on a gated graph neural network, which obtains data of CPU performance bottleneck related indexes of the same architecture of multiple C language projects, and constructs a data set; C functions are extracted from the C language projects and an abstract syntax tree is constructed; data flow information, control flow information and code sequence information are integrated in the abstract syntax tree; code information graphs are obtained; then a deep learning model based on the gated graph neural network is built and trained, and finally, CPU performance bottleneck related indexes of the C functions are predicted under the same architecture as the data set. The method can statically evaluate CPU performance bottleneck indexes of the C functions without actually running the codes, and does not require developers to write tests, perform compilation and construction and the like for the codes, thereby avoiding time length overhead of a traditional dynamic program analysis method, and improving the efficiency of the developers in CPU performance bottleneck positioning and CPU performance optimization.

The invention provides a charging mode detection method, charging equipment and a storage medium. The method comprises the following steps: the charging communication controller outputs a constant electric signal to each of the first pin and the second pin after being electrified, and detects a corresponding voltage; judging whether the voltage of the first pin is within a first threshold value corresponding to the first charging mode or not; if yes, establishing connection with the charging pile based on the first charging mode; if not, whether the voltage of the second pin is within a second threshold value corresponding to the second charging mode or not is judged; and if yes, establishing connection with the charging pile based on a second charging mode, and if not, determining the mode as an unknown mode. On the basis, the charging mode is directly identified through the physical layer, so that the detection efficiency is improved, and risks such as abnormal operation of the CAN transceiver caused by mismatching of the physical layer and the like are eliminated; in addition, filtering processing is carried out on voltage sampled for multiple times, contact jitter and electromagnetic interference at the moment of connection are effectively restrained, and the stability and reliability of a detection result are ensured.