Yangmin Ding is a Researcher in the Optical Networking and Sensing Department at NEC Laboratories America in Princeton, NJ. He received his PhD in Civil and Environmental Engineering from Rutgers University, his MS in Highway and Railway Engineering from Southeast University, and his BS in Civil Engineering from Changsha University of Science and Technology. Dr. Ding’s research centers on advancing distributed fiber optic sensing (DFOS) technologies, with an emphasis on extracting high-resolution, real-time insights from existing optical fiber networks. His research explores innovative methods for fault localization in the power grid and the integration of DFOS data with critical infrastructure monitoring. He also investigates the application of generative AI to improve operational efficiency, developing intelligent agents for specialized knowledge retrieval and workflow assistance. Dr. Ding’s research creates a new paradigm for infrastructure management by fusing classical structural analysis with the power of AI and advanced sensing. His work highlights the potential of optical networks not only as communication backbones but also as pervasive sensor arrays for applications in smart grids, environmental monitoring, and infrastructure resilience.
For the first time, we demonstrate detection and classification of rain intensity using Distributed Acoustic Sensing (DAS). An artificial neural network was applied for rain intensity classification and high precision of over 96% was achieved.
For the first time to our knowledge, a stationary weight hanging on an operational aerial telecommunication field fiber was detected and localized using only ambient data collected by a φ-DAS system. Although stationary weights do not create temporally varying signals, and hence cannot be observed directly from the DAS traces, the existence and the location of the additional weights were revealed by the operational modal analysis of the aerial fiber structure.
We demonstrated for the first time to our knowledge, the detection and localization of a static weight on an aerial cable by using frequency domain decomposition analysis of ambient vibrations detected by a φ-DAS system.
In distributed acoustic sensing (DAS) on aerial fiber-optic cables, utility pole localization is a prerequisite for any subsequent event detection. Currently, localizing the utility poles on DAS traces relies on human experts who manually label the poles’ locations by examining DAS signal patterns generated in response to hammer knocks on the poles. This process is inefficient, error-prone and expensive, thus impractical and non-scalable for industrial applications. In this paper, we propose two machine learning approaches to automate this procedure for large-scale implementation. In particular, we investigate both unsupervised and supervised methods for fine-grained pole localization. Our methods are tested on two real-world datasets from field trials, and demonstrate successful estimation of pole locations at the same level of accuracy as human experts and strong robustness to label noises.
We demonstrate fiber optic sensing systems in a distributed fiber sensor network built on existing telecom infrastructure to detect temperature, acoustic effects, vehicle traffic, etc. Measurements are also demonstrated with different network topologies and simultaneously sensing four fiber routes with one system.
