2results about How to "Reduce performance indicators" patented technology

The invention provides a track irregularity signal compression and reconstruction method and system based on a compressed sensing theory, and mainly relates to the technical field of railway infrastructure health monitoring and big data processing. According to the method, the sampling accuracy in the engineering field is mainly improved, redundant information is reduced, the system bottleneck of storage and transmission is improved, a measurement matrix irrelevant to a sparse transformation base is introduced at a signal acquisition end, and a compression observation value far lower than the Nyquist rate is directly obtained. And then, an original track irregularity signal is reconstructed from a small number of observation values with high precision by solving a norm minimization problem at a data processing end. According to the method, the front-end data sampling rate, the hardware load and the data transmission and storage requirements are greatly reduced, meanwhile, it is guaranteed that the reconstructed signal meets the engineering analysis precision, and a core technical scheme is provided for a new-generation efficient and low-cost track detection system, real-time train-structure system dynamic analysis and the like.

This invention proposes a diagnostic method for autism spectrum disorder (ASD) based on adaptive fusion of multiple graph Transformers, targeting resting-state functional magnetic resonance imaging (rs-fMRI) brain network analysis scenarios. This method constructs a hybrid GCN-Transformer architecture, simultaneously modeling local topological features and global long-range dependencies in the brain network, and introduces bidirectional attention and a two-stream adaptive fusion mechanism to dynamically integrate complementary information from multiple functional connectivity patterns, such as Pearson correlation, sparse representation, and Granger causality, achieving efficient identification of complex neuropathological features of ASD. Experimental results show that the method achieves a classification accuracy of 94.7% on the ABIDE public dataset, a 3.6% improvement over existing state-of-the-art methods. Its innovation lies in: the first application of hybrid GCN-Transformers to brain network analysis, the proposal of a data-driven dynamic fusion strategy to replace traditional static fusion, and the systematic integration of multiple brain connectivity patterns, providing a more comprehensive and effective solution for intelligent diagnosis of brain diseases.