Segmentation-Oriented Compressed Sensing for Efficient Impact Damage Detection on CFRP Materials

Autor:	Gui Yun Tian, Jianbo Wu, Chaoqing Tang
Rok vydání:	2021
Předmět:	Microwave imaging Compressed sensing Noise measurement Control and Systems Engineering Computer science Electronic engineering Image segmentation Iterative reconstruction Electrical and Electronic Engineering Raster scan Matching pursuit Computer Science Applications Sparse matrix
Zdroj:	IEEE/ASME Transactions on Mechatronics. 26:2528-2537
ISSN:	1941-014X 1083-4435
DOI:	10.1109/tmech.2020.3041768
Popis:	The emerging task-oriented compressed sensing (CS) provides a possibility to revolutionize traditional separate sensing-processing models by jointly considering task with data and system. This article presents an efficient mechatronic system for impact damage detection on carbon fiber reinforced polymer materials. By jointly designing the damage region segmentation task with the near-field microwave imaging system, the overall efficiency is improved by one order of magnitude without upgrading the hardware. The proposed segmentation-oriented CS greatly down-samples the specimens with a specially designed binary measurement matrix, followed by a corresponding segment-orthogonal matching pursuit process that obtains the defect region directly from under-sampled data. Compared to raster scan and the traditional CS designs, the proposed method eliminates the redundant process of obtaining the full data and defect region extraction algorithms. Segmentation-oriented CS achieves the best speed and accuracy that single method cannot, and it is much more robust to noise or measurement fault. This integrated sensing-processing method can inspire more efficient mechatronics applications.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::87d2960e3001afee22f52e9a6dfa2282 https://doi.org/10.1109/tmech.2020.3041768 Zobrazit plný text záznamu