Yangmin Ding NEC Labs America

Yangmin Ding

Researcher

Optical Networking & Sensing

Posts

Unearthing Nature’s Orchestra – How Fiber Optic Cables Can Hear Cicada Secrets

Our Sarper Ozharar, Yue Tian and Yangmin Ding and Jessica L. Ware from the American Museum of Natural History have discovered that fiber optic cables equipped with distributed acoustic sensing (DAS) can pick up the sounds of Brood X cicadas. DAS technology, typically used to monitor seismic activity, can detect the vibrations caused by the loud sounds of cicadas, which live underground for years until they come up to mate.

Long Term Monitoring and Analysis of Brood X Cicada Activity by Distributed Fiber Optic Sensing Technology

Brood X is the largest of the 15 broods of periodical cicadas, and individuals from this brood emerged across the Eastern United States in spring 2021. Using distributed acoustic sensing (DAS) technology, the activity of Brood X cicadas was monitored in their natural environment in Princeton, NJ. Critical information regarding their acoustic signatures and activity level is collected and analyzed using standard outdoor-grade telecommunication fiber cables. We believe these results have the potential to be a quantitative baseline for regional Brood X activity and pave the way for more detailed monitoring of insect populations to combat global insect decline. We also show that it is possible to transform readily available fiber optic networks into environmental sensors with no additional installation costs. To our knowledge, this is the first reported use case of a distributed fiber optic sensing system for entomological sciences and environmental studies.

Beyond Communication: Telecom Fiber Networks for Rain Detection and Classification

We present the field trial of an innovative neural network and DAS-based technique, employing a pre-trained CNN fine-tuning strategy for effective rain detection and classification within two practical scenarios.

A Temperature-Informed Data-Driven Approach for Behind-the-Meter Solar Disaggregation

The lack of visibility to behind-the-meter (BTM) PVs causes many challenges to utilities. By constructing a dictionary of typical load patterns based on daily average temperatures and power consumptions, this paper proposes a temperature-informed data-driven approach for disaggregating BTM PV generation. This approach takes advantage of the high correlation between outside temperature and electricity consumption, as well as the high similarity between PV generation profiles. First, temperature-based fluctuation patterns are extracted from customer load demands without PV for each specific temperature range to build a temperature-based dictionary (TBD) in the offline stage. The dictionary is then used to disaggregate BTM PV in real-time. As a result, the proposed approach is more practical and provides a useful guideline in using temperature for operators in online mode. The proposed methodology has been verified using real smart meter data from London.

Utility Pole Localization by Learning From Ambient Traces on Distributed Acoustic Sensing

Utility pole localization by learning from ambient traces on distributed acoustic sensing Utility pole detection and localization is the most fundamental application in aerial-optic cables using distributed acoustic sensing (DAS). The existing pole localization method recognizes the hammer knock signal on DAS traces by learning from knocking vibration patterns. However, it requires many efforts for data collection such as knocking every pole and manually labeling the poles’ locations, making this labor-intensive solution expensive, inefficient, and highly error prone. In this paper, we propose a pole localization solution by learning the ambient data collected from a DAS system, which are vibration patterns excited by random ambient events, such as wind and nearby traffic. In detail, we investigate a universal framework for learning representations of ambient data in the frequency domain by contrastive learning of the similarity of low and high-frequency series. A Gaussian-based data reweighting kernel is employed for eliminating the effect of the label noise. Experimental results demonstrate the proposed methods outperform the existing contrastive learning methods on the real-world DAS ambient dataset.

Distributed fiber optic sensing over readily available telecom fiber networks

Distributed fiber optic sensing over readily available telecom fiber networks Distributed Fiber Optic Sensing (DFOS) systems rely on measuring and analyzing different properties of the backscattered light of an optical pulse propagating along a fiber cable. DFOS systems can measure temperature, strain, vibrations, or acoustic excitations on the fiber cable and to their unique specifications, they have many applications and advantages over competing technologies. In this talk we will focus on the challenges and applications of DFOS systems using outdoor grade telecom fiber networks instead of standard indoor or some specialty fiber cables.

Semi-supervised Identification and Mapping of Water Accumulation Extent using Street-level Monitoring Videos

Semi-supervised Identification and Mapping of Water Accumulation Extent using Street-level Monitoring Videos Urban flooding is becoming a common and devastating hazard, which causes life loss and economic damage. Monitoring and understanding urban flooding in a highly localized scale is a challenging task due to the complicated urban landscape, intricate hydraulic process, and the lack of high-quality and resolution data. The emerging smart city technology such as monitoring cameras provides an unprecedented opportunity to address the data issue. However, estimating water ponding extents on land surfaces based on monitoring footage is unreliable using the traditional segmentation technique because the boundary of the water ponding, under the influence of varying weather, background, and illumination, is usually too fuzzy to identify, and the oblique angle and image distortion in the video monitoring data prevents georeferencing and object-based measurements. This paper presents a novel semi-supervised segmentation scheme for surface water extent recognition from the footage of an oblique monitoring camera. The semi-supervised segmentation algorithm was found suitable to determine the water boundary and the monoplotting method was successfully applied to georeference the pixels of the monitoring video for the virtual quantification of the local drainage process. The correlation and mechanism-based analysis demonstrate the value of the proposed method in advancing the understanding of local drainage hydraulics. The workflow and created methods in this study have a great potential to study other street level and earth surface processes.

Finite Element Modeling of Pavement and State Awareness Using Fiber Optic Sensing

Finite Element Modeling of Pavement and State Awareness Using Fiber Optic Sensing A variety of efforts have been put into sensing and modeling of pavements. Such capability is commonly validated with experimental data and used as reference for damage detection and other structural changes. Finite element models (FEM) often provides a high fidelity physics-base benchmark to evaluate the pavement integrity. On the monitoring of roads and pavements in general, FEM combining with in-situ data largely extends the awareness of the pavement condition, and enhances the durability and sustainability for the transportation infrastructures. Although many studies were performed in order to simulate static stress and strain in the pavement, FEM also show potential for dynamic analysis, allowing to extract both frequency response and wave propagation at any location, including the behavior of the soil on the surroundings. Fiber optical sensing is adopted in this research, which outperforms the traditional sensing techniques, such as accelerometers or strain gauges, given its nature of providing continuous measurement in a relatively less intrinsic fashion. Moreover, the data is adopted to validate and calibrate the FEM with complex material properties, such as damping and viscoelasticity of the pavement as well as other nonlinear behavior of the surrounded soil. The results demonstrate a successful FEM with good accuracy of the waveform prediction.

Vibration-Based Status Identification of Power Transmission Poles

Vibration-Based Status Identification of Power Transmission Poles Among the power transmission infrastructures, the low-voltage overhead power lines are specifically critical, due to the complicated roadside environments and the significant number of connections to the end utility users. Maintaining of such a large size grid with mostly wood poles is a challenging task and knowing the operating status and its structural integrity drastically speeds up the routine inspection. Applying a data-driven approach using accelerometer data to analyze the power line-induced vibration to classify different poles within different operational conditions is proposed.Feature creation is the important aspect to improve an accuracy of data-driven algorithms. For this purpose, a time-frequency domain classifier is developed, based on the data collected from two tri-axial accelerometers installed on the wood poles before and after streetlights are on. Data are explored both in time and frequency domain using techniques such as data augmentation and segmentation, averaging, filtering, and principal component analysis. Results of the machine learning classifier clearly shows distinct characteristics among the data collected from different work conditions and different poles. Further exploration of the applied algorithm will be pursued to construct more sophisticated features based on supervised learning to enhance the identification accuracy.

Rain Intensity Detection and Classification with Pre-existing Telecom Fiber Cables

Rain Intensity Detection and Classification with Pre-existing Telecom Fiber Cables 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.