Feature analysis of acoustic emission sources for rail crack detection by the finite element method

The safety of rail is very important for the development of high speed railway, and it is necessary to investigate the features of inner cracks in rail. In order to obtain the features of Acoustic Emission （AE） sources of inner cracks in rail, AE sources with different types, depths and propagation distances are examined for crack in rail. The finite element method is utilized to model the rail with cracks and the results of experiment demonstrate the effectiveness of this model. Wavelet transform and Rayleigh-Lamb equations are utilized to extract the features of crack AE sources. The results illustrate that the intensity ratio among AE modes can identify the AE source types and the AE sources with different frequencies in rail. There are uniform AE mode features existing in the AE signals from AE sources in rail web, however AE signals from AE sources in rail head and rail base have the complex and unstable AE modes. Different AE source types have the different propagation features in rail. It is helpful to understand the rail cracks and detect the rail cracks based on the AE technique.

Element-Arrangement Context Network for Facade Parsing

Facade parsing aims to decompose a building facade image into semantic regions of the facade objects.Considering each architectural element on a facade as a parameterized rectangle,we formulate the facade parsing task as object detection,allowing overlapping and nesting,which will support structural 3D modeling and editing for further applications.In contrast to general object detection,the spatial arrangement regularity and appearance similarity between the facade elements of the same category provide valuable context for accurate element localization.In this paper,we propose to exploit the spatial arrangement regularity and appearance similarity of facade elements in a detection framework.Our element-arrangement context network(EACNet)consists of two unidirectional attention branches,one to capture the column-context and the other to capture row-context to aggregate element-specific features from multiple instances on the facade.We conduct extensive experiments on four public datasets(ECP,CMP,Graz50,and eTRIMS).The proposed EACNet achieves the highest mIoU(82.1%on ECP,77.35%on Graz50,and 82.3%on eTRIMS)compared with the state-of-the-art methods.Both the quantitative and qualitative evaluation results demonstrate the effectiveness of our dual unidirectional attention branches to parse facade elements.