Left ventricular ejection fraction (LVEF) is a critical indicator for assessing cardiac function and diagnosing heart disease. LVEF can be derived by estimating the left ventricular volume from end-systolic and end-diastolic frames through echocardiography segmentation. However, current algorithms either focus primarily on single-frame segmentation, neglecting the temporal and spatial correlations between consecutive frames, or often fail to effectively address the inherent challenges posed by the low-contrast and fuzzy edges characteristic of echocardiography, thereby resulting in suboptimal segmentation outcomes. In this study, we propose a novel self-attention-based dual-task learning approach for automatic left ventricle segmentation. First, we introduce a multi-scale edge-attention U-Net to achieve supervised semantic segmentation of echocardiography. Second, an optical flow network is developed to capture the changes in the optical flow fields between frames in an unsupervised manner. These two tasks are then jointly trained using a temporal consistency mechanism to extract spatio-temporal features across frames. Experimental results demonstrate that our model outperforms existing segmentation methods. Our proposed method not only enhances the performance of semantic segmentation but also improves the consistency of segmentation between consecutive frames.

When identifying the signal waveform, it is interfered by the clutter in the original power fault record, which leads to the low accuracy of the identification result. Therefore, the research of power fault record waveform identification based on instantaneous characteristic parameters is proposed. A multicomponent real-valued power fault recorded signal is decomposed in a non-recursive way, which is transformed into a discrete quasi-orthogonal limited bandwidth sub-signal set, and the effective bandwidth is extracted as the target instantaneous characteristic parameter. In the phase of waveform identification, the unsupervised adaptive framework of countermeasure domain (FA-AUDA) is applied, and the countermeasure network (CGAN) is generated with the help of specific conditions (power failure), so as to enhance the instantaneous characteristic parameters of source domain data in the feature space and realize waveform identification. In the test results, the design method has the highest accuracy for the identification of power fault waveform in different crossing States, and it is always stable above 0.70.