The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 01, 2025

Filed:

Nov. 15, 2022
Applicant:

Nec Laboratories America, Inc., Princeton, NJ (US);

Inventors:

Yuheng Chen, South Brunswick, NJ (US);

Ming-Fang Huang, Princeton, NJ (US);

Ting Wang, West Windsor, NJ (US);

Jingnan Zhao, Edison, NJ (US);

Assignee:

NEC Corporation, Tokyo, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01H 9/00 (2006.01); G01P 3/36 (2006.01); G06N 3/08 (2023.01); G06N 20/10 (2019.01);
U.S. Cl.
CPC ...
G01H 9/004 (2013.01); G01P 3/36 (2013.01); G06N 3/08 (2013.01); G06N 20/10 (2019.01);
Abstract

A fiber optic sensing cable located along a side of a paved road and runs parallel to a driving direction is monitored by distributed fiber optic sensing (DFOS) using Rayleigh backscattering generated along the length of the optical sensor fiber cable under dynamic vehicle loads. The interaction of vehicles with roadway locations exhibiting distressed pavement generates unique patterns of localized signals that are identified/distinguished from signals resulting from vehicles operating on roadway exhibiting a smooth pavement surface. Machine learning methods are employed to estimate an overall road surface quality as well as localizing pavement damage. Power spectral density estimation, principal component analysis, support vector machine (SVM) combined with principal component analysis (PCA), local binary pattern (LBP), and convolutional neural network (CNN) are applied to develop the machine learning models.


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