公开(公告)号：US20250130076A1

公开(公告)日：2025-04-24

申请号：US18901690

申请日：2024-09-30

Applicant: NEC Laboratories America, Inc.

Inventor： Jian FANG ,  Ting WANG ,  Junyi YE

IPC: G01D5/353 ,  G01D5/347

Abstract: Disclosed are systems, methods, and structures that mitigate accumulative error in relative-measurement DFOS by employing, for each segment of the DFOS arrangement that records a certain number of an earlier estimation of each spatial segment. A predictive model in each buffer learns trends from the recorded history and predicts an output from the previous history. The reference is also updated using the prediction from the buffer. Systems, methods, and structures according to the present disclosure include the buffer structure that records a certain number of earlier estimations for each segment, a predictive model in each buffer that predicts the output of each segment according to the earlier estimations, reference updates using the prediction from the buffer tracker and workflow of real-time data processing with the buffer structure and tracker.

公开(公告)号：US20250124345A1

公开(公告)日：2025-04-17

申请号：US18759965

申请日：2024-06-30

Applicant: NEC Laboratories America, Inc.

Inventor： Jian FANG ,  Ting WANG ,  Junyi YE

IPC: G06N20/00

Abstract: Disclosed are systems, methods, and structures that provide more accurate temperature measurements and/or derived measurements using distributed fiber optic sensing (DFOS) systems and methods. DFOS systems and methods according to aspects of the present disclosure employ distributed fiber optic sensing that determines real-time temperature changes and vehicle trajectories from two-dimensional (2D) DFOS data with very few labeled data. The 2D data is first divided into multiple grids and then pre-processed with image distortion methods to enrich diversity of temperature change patterns. The transformed grids are used to pre-train a masked autoencoder, which advantageously does not require labels. The encoder of the autoencoder learns intrinsic features of temperature and traffic patterns, which are later connected to an estimation network to solve downstream tasks trained on a small set of labeled data.