Ping Lu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Ping Lu has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Abhishek Venketeswaran
National Energy Technology Laboratory, 626 Cochrans Mill Road, Pittsburgh, PA 15236, USA
1 shared publication - Michael P. Buric
NETL
1 shared publication - Nageswara Lalam
National Energy Technology Laboratory, Pittsburgh, PA, USA
1 shared publication - Ruishu Wright
NETL
1 shared publication - Sandeep Reddy Bukka
National Energy Technology Laboratory, Pittsburgh, PA, USA
1 shared publication
Latest Publications
- Open Access
Robust Vector BOTDA Signal Processing with Probabilistic Machine Learning
This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain and phase spectra of a vector Brillouin optical time-domain analysis (VBOTDA). The PML framework is used to predict the Brillouin frequency shift (BFS) along the fiber and to assess its predictive uncertainty. We compare the predictions obtained from the proposed PML model with a conventional curve fitting method and evaluate the BFS uncertainty and data processing time for both methods. The proposed method is demonstrated using two BOTDA systems: (i) a BOTDA system with a 10 km sensing fiber and (ii) a vector BOTDA with a 25 km sensing fiber. The PML framework provides a pathway to enhance the VBOTDA system performance.
Open AccessRobust Vector BOTDA Signal Processing with Probabilistic Machine Learning
This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain and phase spectra of a vector Brillouin optical time-domain analysis (VBOTDA). The PML framework is used to predict the Brillouin frequency shift (BFS) along the fiber and to assess its predictive uncertainty. We compare the predictions obtained from the proposed PML model with a conventional curve fitting method and evaluate the BFS uncertainty and data processing time for both methods. The proposed method is demonstrated using two BOTDA systems: (i) a BOTDA system with a 10 km sensing fiber and (ii) a vector BOTDA with a 25 km sensing fiber. The PML framework provides a pathway to enhance the VBOTDA system performance.