Conventionally image translation is used to convert synthetic aperture radar (SAR) images to optical ones to increase interpretability. Due to the different imaging natures of SAR range sensing and optical directional projection, it is within the expectation that some kinds of SAR images could be reasonably translated into optical ones, while others not. Arguably, less good patches in the translated image could act as a potential indicator of salient differences of the two imaging mechanisms. Following this line of thought, considering in urban SAR images, the layover is mainly caused by buildings, we proposed a method to detect building layovers in SAR images by evaluating the correlation of the original SAR image and the translated one by the cycle-translation, that is, from SAR to optical to SAR translation. Because the presence of building is the main factor of the layover phenomenon in urban SAR images, building layover areas in SAR images are expected to be less correlated, or they should have a low correlation coefficient. Preliminary experiments validate our underlying principle and method.
Path planning algorithm is a key research problem in the application of autonomous mobile robots. RRT algorithm is one of the excellent methods for robot path planning. However, RRT algorithm has drawbacks such as high time consumption, high number of samples, low operational efficiency. On the basis of an adaptive resolution Octree Map, this paper presents an improved RRT path planning algorithm through establishing a growth point evaluation function. By adding the restriction through the evaluation function to random growing points of RRT, the random growth can be turned into purpose-oriented, and redundant growth points and routes can be eliminated. Besides, the growth path is reselected and rewired by the parent node and candidate nodes obtained through the growing process. Through reselection and rewiring, the route is continuously optimized and smoothed. Experiments are carried out for performance evaluation. Experiment results indicate that, comparing with the traditional RRT, RRT* and B-RRT*, the improved RRT algorithm can eliminate the redundant bifurcations of the growth tree reduce the number of sampling times, and greatly improves the growth efficiency.
Safety issues caused by material defects have always been a hot issue that has attracted much attention. How to realize rapid and accurate identification of material defects is the focus of research on material defects today. Barkhausen noise measurement is a new material performance testing technology, which is suitable for ferromagnetic materials and is sensitive to many characteristics of the material. This paper builds a Barkhausen inspection system to detect and quantitatively analyze the defects of different widths and depths in ferromagnetic materials and explore the influence of different excitation parameters of the inspection system on the Barkhausen signal.
The stress of weld in pressure vessel is complex and the working conditions are poor. The conventional nondestructive testing methods cannot meet the requirements of weld surface defect detection. In this paper, eddy current pulse thermal imaging method is used to study the detection technology of weld surface defects. The finite element simulation software is used to analyze the electromagnetic induction eddy current and temperature distribution at the weld crack. Build an experimental platform to verify the feasibility of eddy current pulse thermal imaging to detect weld cracks. In order to eliminate the background noise of the thermal image, the detection result adopts the absolute temperature rise method to improve the detection accuracy.