中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

42 records (took 0.036 seconds)

    DOMAIN GENERALIZATION FOR CROSS-DOMAIN RAIN INTENSITY DETECTION BASED ON DISTRIBUTED FIBER OPTIC SENSING (DFOS)
    1.
    发明申请
    DOMAIN GENERALIZATION FOR CROSS-DOMAIN RAIN INTENSITY DETECTION BASED ON DISTRIBUTED FIBER OPTIC SENSING (DFOS) 有权

    公开(公告)号：US20250130349A1

    公开(公告)日：2025-04-24

    申请号：US18901719

    申请日：2024-09-30

    Applicant: NEC Laboratories America, Inc.

    Inventor： Zhuocheng JIANG ,  Yangmin DING ,  Yue TIAN ,  Sarper OZHARAR ,  Ting WANG ,  Yanchao Wang

    IPC: G01W1/14 ,  G01H9/00

    Abstract: Disclosed are systems, methods, and structures that provide superior DFOS rain intensity measurements and introduce a universal solution for rain intensity detection based on the data collected by distributed acoustic sensing (DAS) technology and a designed domain generalization method. As a result, systems and methods according to the present disclosure distinguish the rain intensity of a large area through which the fiber optic cables traverse and address the domain shift issue, by employing a domain generalization technique based on machine learning technology in which newly collected target domain inference data may be distributed differently from the previously captured training source domain data. To generalize the trained model to different target domains, source domain distributions are enriched by disturbing the distribution in the frequency domain. Algorithms specifically designed to transfer the noise pattern under ambient noise environments are used to further augment the source domain distributions.

    DYNAMIC LINE RATING (DLR) OF OVERHEAD TRANSMISSION LINES
    2.
    发明公开
    DYNAMIC LINE RATING (DLR) OF OVERHEAD TRANSMISSION LINES 审中-公开

    公开(公告)号：US20240133937A1

    公开(公告)日：2024-04-25

    申请号：US18485235

    申请日：2023-10-11

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yangmin DING ,  Ting WANG ,  Yue TIAN ,  Sarper OZHARAR

    IPC: G01R31/08

    CPC classification number: G01R31/085 ,  G01R31/088

    Abstract: Systems, methods, and structures providing dynamic line rating (DLR) for overhead transmission lines based on distributed fiber optic sensing (DFOS)/distributed temperature sensing (DTS) to determine temperature of the electrical conductors. Environmental conditions such as wind speed, wind direction, and solar radiation data, are collected from environmental sensors and an acoustic modem that convert the digital data collected from the environmental sensors into generated vibration patterns that are subsequently used to vibrationally excite a DFOS optical sensor fiber. The DFOS system monitors the optical sensor fiber and detects, measures, and decodes the vibrational excitations. An Artificial Neural Network (ANN) determines a heat transfer correlation between the temperature of the optical sensor fiber and electrical conductor(s) (core temperature).

    JOINT COMMUNICATION AND SENSING FOR FALLEN TREE LOCALIZATION ON OVERHEAD LINES
    3.
    发明公开
    JOINT COMMUNICATION AND SENSING FOR FALLEN TREE LOCALIZATION ON OVERHEAD LINES 审中-公开

    公开(公告)号：US20240085238A1

    公开(公告)日：2024-03-14

    申请号：US18501203

    申请日：2023-11-03

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yangmin DING ,  Zhuocheng JIANG ,  Sarper OZHARAR ,  Yue TIAN ,  Ting WANG

    IPC: G01H9/00 ,  G01D5/353

    CPC classification number: G01H9/004 ,  G01D5/35361

    Abstract: In sharp contrast to the prior art, a fallen tree detection and localization method based on distributed fiber optical sensing (DFOS) technique and physics informed machine learning is described in which DFOS leverages existing fiber cables that are conventionally installed on the bottom layer of distribution lines and used to provide high-speed communications. The DFOS collects and transmits fallen tree induced vibration data along the length of the entire overhead lines, including distribution lines and transmission lines, where there is a fiber cable deployed. The developed physics-informed neural network model processes the data and localizes the fallen tree location along the lines. The location is interpreted in at least two aspects: the fallen tree location in terms of the fiber cable length; and the exact cable location (power cable or fiber cable) that the fallen tree mechanically impacts.

    IDENTIFICATION OF FALSE TRANSFORMER HUMMING USING MACHINE LEARNING
    5.
    发明申请
    IDENTIFICATION OF FALSE TRANSFORMER HUMMING USING MACHINE LEARNING 有权

    公开(公告)号：US20230024104A1

    公开(公告)日：2023-01-26

    申请号：US17871862

    申请日：2022-07-22

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yangmin DING ,  Sarper OZHARAR ,  Yue TIAN ,  Ting WANG

    IPC: G01R31/62 ,  G01H9/00

    Abstract: Systems, and methods for automatically determining false transformer humming when using DFOS systems and methods to determine such humming along with machine learning approach(es) to identify the false transformer humming signal(s) that are transferred to a utility pole without a transformer from a working transformer on another utility pole. Advantageously, our inventive systems and methods employ a customized signal processing workflow to process raw data collected from the DFOS. Our employs a binary classifier that can automatically identify a transformer humming signal from a utility pole with a transformer and simultaneously identify the false humming signal from a utility pole without a transformer.

    UTILITY POLE LOCALIZATION FROM AMBIENT DATA
    7.
    发明公开
    UTILITY POLE LOCALIZATION FROM AMBIENT DATA 审中-公开

    公开(公告)号：US20240125954A1

    公开(公告)日：2024-04-18

    申请号：US18485240

    申请日：2023-10-11

    Applicant: NEC Laboratories America, Inc.

    Inventor： Zhuocheng JIANG ,  Yue TIAN ,  Yangmin DING ,  Sarper OZHARAR ,  Ting WANG

    IPC: G01V1/00 ,  G01V1/22 ,  G01V1/32

    CPC classification number: G01V1/001 ,  G01V1/226 ,  G01V1/325 ,  G01V2210/43

    Abstract: Systems and methods for utility pole localization employing a DFOS/DAS interrogator located at one end of an optical sensor fiber remotely capture dynamic strains on the optical sensor fiber induced by acoustic events. A captured two-dimensional spatiotemporal map in an ambient noisy environment is analyzed by a trained machine learning model which then automatically detects an area in which a pole is located without requiring domain knowledge. Original DFOS/DAS signals are separated into pole regions and non-pole region time series for machine learning model training. A contrastive loss function measures similarities between low-frequency and high-frequency features. A Gaussian distribution is applied to the original signals to generate weighted labels to eliminate effects of label noise. The machine learning model fuses low-frequency and high-frequency features in the frequency domain for pole region classification. A contrastive loss is combined with cross entropy loss to measure a low-high frequency feature distance.

    AUDIO BASED WOODEN UTILITY POLE DECAY DETECTION BASED ON DISTRIBUTED ACOUSTIC SENSING AND MACHINE LEARNING
    8.
    发明公开
    AUDIO BASED WOODEN UTILITY POLE DECAY DETECTION BASED ON DISTRIBUTED ACOUSTIC SENSING AND MACHINE LEARNING 审中-公开

    公开(公告)号：US20230266196A1

    公开(公告)日：2023-08-24

    申请号：US18113023

    申请日：2023-02-22

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yangmin DING ,  Yue TIAN ,  Sarper OZHARAR ,  Ting WANG

    IPC: G01M5/00

    CPC classification number: G01M5/0033 ,  G01M5/0025 ,  G01M5/0066

    Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) systems, methods, and structures that employ machine learning and provide for the automatic remote inspection and condition evaluation of wooden utility poles. Operationally, audio (acoustic) signals are obtained using DFOS/DAS when a service technician/inspector strikes the wooden utility poles with an impact tool such as a hammer. Historical audio DFOS/DAS signals that include signals resulting from hollow (decayed) utility poles and solid (good) poles are used to train one or more machine learning models and the trained machine learning models are subsequently used to evaluate real-time impact data collected from DFOS/DAS and determine utility pole condition in real-time.

    CONTRASTIVE LEARNING OF UTILITY POLE REPRESENTATIONS FROM DISTRIBUTED ACOUSTIC SENSING SIGNALS
    10.
    发明申请
    CONTRASTIVE LEARNING OF UTILITY POLE REPRESENTATIONS FROM DISTRIBUTED ACOUSTIC SENSING SIGNALS 有权

    公开(公告)号：US20220381607A1

    公开(公告)日：2022-12-01

    申请号：US17714091

    申请日：2022-04-05

    Applicant: NEC Laboratories America, Inc.

    Inventor： Shaobo HAN ,  Yue TIAN ,  Sarper OZHARAR ,  Yangmin DING ,  Ting WANG

    IPC: G01H9/00

    Abstract: A testing procedure including a data collection procedure and a contrastive learning-based approach, for establishing a profile for utility poles surveyed in an embedding space. Unique properties of utility poles are preserved in a low-dimensional feature vector. Similarities between pairs of samples collected at the same or different poles is reflected by the Euclidean distance between the pole embeddings. During data collection—variabilities of excitation signals are manually introduced, e.g. impact strength, impact locations, impact time ambiguity, data collecting location ambiguity on a DFOS/DAS optical sensor fiber/cable. Data so collected provides a learned model learned complete information about a utility pole and is more robust with respect to uncontrollable factors during operation. A model training procedure that effectively extracts a utility pole intrinsic properties (e.g., structure integrity, dimensions, structure variety) and remote extrinsic influence (e.g., excitation strength, weather conditions, road traffic), without knowing the ground truth of these factors. The only identifying label required is an ID of any tested poles, which is readily available. The model is trained adaptively—end-to-end—is advantageously easy-to-implement on modern deep learning frameworks such as PyTorch.

Patent Agency Ranking