Nec Corporation

Yangmin Ding

Yue Tian

Sarper Ozharar

Ting Wang

Shaobo Han

Yuanda Xu

Zhuocheng Jiang

Jiatong Li

You Lu

Yuandu Xu

Disclosed are systems and methods to determine barometric pressure 1) using multiple low-cost pressure sensors located at known heights instead of a single high-cost sensor; 2) determines an actual pressure value—not by averaging multiple sensors but rather optimizing an expected error in each individual one of them and utilize their known sensor heights thereby defining a new error function; 3) our approach is scalable, i.e. the number of sensors can be increased, and multiple sensors can be grouped together into smaller cells such that each group of cell can be corrected separately, and can even be corrected among themselves. Finally, our systems and methods according to the present disclosure can advantageously be integrated with a distributed fiber optic sensing (DFOS) system via acoustic modems thereby providing extremely wide-area external, or interior buildings pressure readings.

Low-cost high precision barometric pressure measurement system

A distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) system and method employing a fiber optic sensor cable that collects vibrational data of individual utility poles suspending the fiber optic sensor cable and stores the vibrational data in a central office (CO). Machine learning (ML) models are developed, trained, and utilized to analyze vibrational features of the utility poles and determine their integrity. Additionally, DFOS/DAS systems and methods according to the present disclosure determine the location(s) of fiber coils that exist along a length of a fiber optic sensor cable.

System to measure coil locations and lengths on aerial fiber cables by distributed fiber sensing for decision making

Computer vision based, wide-area snow/water level estimation methods using disparity maps. In one embodiment our method provides rich depth-information using a stereo camera and image processing. Scene images at normal and snow/rain weather conditions are obtained by a double-lens stereo camera and a disparity map is generated from the scene images at left and right lenses using a self-supervised deep convolutional network. In another embodiment, our method uses a single point snow/water level sensor, a stationary monocular camera to measure snow/water levels covering a wide area.

Computer vision based wide-area snow/water level estimation using disparity map

A radio-controlled, two-way acoustic modem for operating with a distributed fiber optic sensing (DFOS) system including circuitry that receives radio signals including configuration information, configures the modem to operate according to the configuration information, and generate acoustic signals that are detected by the DFOS system. The acoustic modem includes one or more sensors that detect environmental information that is encoded in the acoustic signals for further reception by the DFOS system. The received configuration information may change the operating times, sensors or other operating aspects of the modem as desired an such information may be transmitted from a fixed location or a mobile vehicle.

Telecom cable tension screening technique based on wave propagation and distributed acoustic sensing

Aspects of the present disclosure describe snow/water level detection using distributed fiber optic sensing/distributed acoustic sensing (DFOS/DAS) and an integrated ultrasonic device that advantageously operates over existing optical telecommunications facilities carrying live telecommunications traffic—or optical facilities deployed specifically for such detection. DFOS/DAS monitoring of snow/water level advantageously monitors large areas with high sensitivity while exhibiting robustness to changing environmental conditions and employs a remote (utility pole or other mounting) mounted ultrasonic sensor/transducer that provides snow/water level data in real-time as a coded vibrational signal.

Snow / water level detection with distributed acoustic sensing integrated ultrasonic device

Aspects of the present disclosure describe the localization of a utility pole by distributed fiber sensing of aerial fiber cable suspended from the utility pole.

Utility pole localization by distributed fiber sensing of aerial fiber cable

Systems and methods for performing the dynamic anomaly localization of utility pole aerial/suspended/supported wires/cables by distributed fiber optic sensing. In sharp contrast to the prior art, our inventive systems and methods according to aspects of the present disclosure advantageously identify a 'location region' on a utility pole supporting an affected wire/cable, thereby permitting the identification and reporting of service personnel that are uniquely responsible for responding to such anomalous condition(s).

Dynamic anomaly localization of utility pole wires

Aspects of the present disclosure describe dynamic road traffic noise mapping using DFOS over a telecommunications network that enables mapping of road traffic-induced noise at any observer location. DFOS is used to obtain instant traffic data including vehicle speed, volume, and vehicle types, based on vibration and acoustic signal along the length of a sensing fiber along with location information. A sound pressure level at a point of interest is determined, and traffic data associated with such point is incorporated into a reference noise emission database and a wave propagation theory for total sound pressure level prediction and mapping. Real-time wind speed using DFOS—such as distributed acoustic sensing (DAS)—is obtained to provide sound pressure adjustment due to the wind speed.

Dynamic road traffic noise mapping using distributed fiber optic sensing (DFOS) over telecom network

Systems and methods for reducing work conflicts is provided. The method includes receiving a vibrational signal from a utility pole; identifying a location and type of field work on the utility pole from one or more features of the vibrational signal utilizing a trained neural network; and communicating the location and type of field work to a third party.

Fiber optic sensing for reduced field work conflicts

A method of utility pole integrity assessment by distributed fiber optic sensing/distributed acoustic sensing (DFOS/DAS) employing existing telecommunications fiber optic cable as a sensor. The fiber optic cable is suspended aerially from a plurality of utility poles and a machine learning model is developed during training by mechanically exciting the utility poles. Once developed, and in sharp contrast to the prior art, the machine learning model—in conjunction with DFOS/DAS operation—determines an integrity assessment for a plurality of the utility poles aerially suspending the fiber optic cable from a mechanical impact of a single pole.

Utility pole integrity assessment by distributed acoustic sensing and machine learning

Distributed fiber optic sensing (DFOS) systems and methods for monitoring electrical power distribution transformers and locating failing/failed/malfunctioning transformers from humming signals indicative of a transformer's operational integrity and are advantageously detected/analyzed via DFOS mechanisms.

Distribution transformer localization and monitoring using distributed fiber optic sensing

Distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) techniques coupled with frequency domain decomposition (FDD) are employed to determine the presence of 120 HZ operating power frequency to determine when street lights are energized and on thereby providing continuous status monitoring of the operational status of street lights and locating affected street lights at a particular utility pole location.

Street light operating status monitoring using distributed optical fiber sensing

Systems and methods for operating a distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) system include a length of optical sensing fiber suspended aerially by a plurality of utility poles and in optical communication with a DFOS interrogator/analyzer. The method includes operating the DFOS/DAS system while manually exciting more than one of the poles to obtain frequency response(s) of the excited poles; contrastively training a convolutional neural network (CNN) with the frequency responses obtained; classifying the utility poles using the contrastively trained CNN; and generating a profile map of the excited poles indicative of the classified utility poles.

Contrastive learning of utility pole representations from distributed acoustic sensing signals

Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously enable and/or facilitate the continuous monitoring and identification of damaged utility poles by employing a DFOS distributed acoustic sensing (DAS) methodology in conjunction with a finite element analysis and operational modal analysis. Of particular advantage and in further contrast to the prior art, systems, methods, and structures according to aspects of the present disclosure utilize existing optical fiber supported/suspended by the utility poles as a sensing medium for the DFOS/DAS operation.

Growing community of inventors

North Brunswick, NJ, United States of America

Yangmin Ding