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.
The 2021 emergence of Brood X cicadas was monitored in situ in our testbed using a DAS system connected to an outdoor telecom fiber over a 16-day period. The spectral and energy characteristics of the cicada calling signal has been measured and analyzed.
For the first time, we present an end-to-end AI framework for data analysis in distributed fiber optic sensing. The proposed model eliminates the need for optical phase computation and outperforms traditional data processing pipelines, achieving over 96% recognition accuracy on a diverse acoustic dataset.
Fault localization in power distribution networks is essential for rapid recovery and enhancing system resilience. While Phasor Measurement Units (PMUs or ?PMUs) providehigh-resolution measurements for precise fault localization, their widespread deployment is cost-prohibitive. Distributed Fiber Optic Sensing (DFOS) offers a promising alternative for event detection along power lines using collocated optical fiber; however, it cannot independently differentiate between events and pinpoint exact fault locations. This paper introduces an innovative framework that combines DFOS with sparsely deployed PMUs for accurate fault localization. The proposed approach first utilizes a Graph Attention Network (GAT) model to capture spatial and temporal correlations from synchronized PMU and DFOS measurements, effectively identifying fault zones. High-spatial- resolution DFOS measurements further refine the fault locationwithin the identified zone. Singular Value Decomposition (SVD) is applied to extract feature vectors from DFOS measurements, enhancing the convergence speed of the GAT model. Thisintegrated solution significantly improves localization accuracy while minimizing reliance on extensive deployment of PMUs.
A 100-meter-long fiber optic cable was installed at the bottom of a water tank at the Davidson Laboratory, together with a hydrophone for reference. The water tank is approximately 2.5 meters deep and 95 meters long; the tank also employs a 6-paddle wavemaker which can generate programmable surface waves. A 155-cm-long model boat weighing 6.5 kilograms was automatically dragged on the surface of the tank via an electrical towing mechanism. The movement of the model boat along the fiber cable and over the hydrophone was recorded using a commercially available NEC Distributed Acoustic Sensing (DAS) system and simultaneously by a hydrophone. The experiments were repeated with and without the artificially generated surface waves. The data obtained from the hydrophone and the DAS system are presented and compared. The results show the compatibility between the DAS data and the hydrophone data. More importantly, ourresults show that it is possible to measure the surface waves and to detect a surface vessel approaching the sensor by only using the time domain analysis in terms of detected total energy over time.
We propose a novel Distributed Fiber Optic Sensing (DFOS) placement strategy tailored to the evolving needs of modern power grids, where fiber cables serve dual purposes: communication and real-time sensing. Our approach integrates a heuristic algorithm, PURE (Power Source-aware Route Exploration), with Integer Linear Programming (ILP) to optimize DFOS placement while addressing power supply constraints. The strategy ensures resilient monitoring across diverse grid scenarios by prioritizing observability during outages and leveraging advancements in fiber infrastructure deployment. Case studies demonstrate the effectiveness of our methodology in maintaining power grid resilience while minimizing deployment costs.
We present a generative AI framework based on a conditional diffusion model for distributed acoustic sensing (DAS) data imputation. The proposed DiffOptics model generates high-quality DAS data of various acoustic events using telecom fiber cables.
For the first time, we demonstrate remote sensing of pole-mounted fuse-cutout blowing in a power grid setup using telecom fiber cable. The proposed frequency-based AI model achieves over 98% detection accuracy using distributed fiber sensing data.
Our optical networking & sending team has arrived in Frankfurt for the 2024 European Conference on Optical Communication (ECOC) and is excited to present many papers and tutorials this week. Please follow this page and on our social media channels for updates.
The various sensing technologies such as cameras LiDAR radar and satellites with advanced machine learning models offers a comprehensive approach to environmental perception and understanding. This paper introduces an innovative Distributed Fiber Optic Sensing (DFOS) technology utilizing the existing telecommunication infrastructure networks for rain intensity monitoring. DFOS enables a novel way to monitor weather condition and environmental changes provides real-time continuous and precise measurements over large areas and delivers comprehensive insights beyond the visible spectrum. We use rain intensity as an example to demonstrate the sensing capabilities of DFOS system. To enhance the rain sensing performance we introduce a Deep Phase-Magnitude Network (DFMN) divide the raw sensing data into phase and magnitude component allowing targeted feature learning on each component independently. Furthermore we propose a Phase Frequency learnable filter (PFLF) for the phase component filtering and conduct standard convolution layers on the magnitude component leveraging the inherent physical properties of optical fiber sensing. We formulate the phase-magnitude channel into a parallel network and subsequently fuse the features for a comprehensive analysis in the end. Experimental results on the collected fiber sensing data show that the proposed method performs favorably against the state-of-the-art approaches.
Our team will be attending CVPR 2024 (The IEEE /CVF Conference on Computer Vision & Pattern Recognition) from June 17-21! See you there at the NEC Labs America Booth 1716! Stay tuned for more information about our participation.
