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    MULTI-MODALITY DATA ANALYSIS ENGINE FOR DEFECT DETECTION
    502.
    发明公开
    MULTI-MODALITY DATA ANALYSIS ENGINE FOR DEFECT DETECTION 审中-公开

    公开(公告)号：US20230152791A1

    公开(公告)日：2023-05-18

    申请号：US17984413

    申请日：2022-11-10

    Applicant: NEC Laboratories America, Inc.

    Inventor： LuAn Tang ,  Yuncong Chen ,  Wei Cheng ,  Haifeng Chen ,  Zhengzhang Chen ,  Yuji Kobayashi

    IPC: G05B23/02 ,  G06N20/00

    CPC classification number: G05B23/0221 ,  G06N20/00 ,  G05B23/0235 ,  G05B23/0237

    Abstract: Systems and methods for defect detection for vehicle operations, including collecting a multiple modality input data stream from a plurality of different types of vehicle sensors, extracting one or more features from the input data stream using a grid-based feature extractor, and retrieving spatial attributes of objects positioned in any of a plurality of cells of the grid-based feature extractor. One or more anomalies are detected based on residual scores generated by each of cross attention-based anomaly detection and time-series-based anomaly detection. One or more defects are identified based on a generated overall defect score determined by integrating the residual scores for the cross attention-based anomaly detection and the time-series based anomaly detection being above a predetermined defect score threshold. Operation of the vehicle is controlled based on the one or more defects identified.

    ROAD SURFACE CONDITIONS DETECTION BY DISTRIBUTED OPTIC FIBER SYSTEM
    503.
    发明公开
    ROAD SURFACE CONDITIONS DETECTION BY DISTRIBUTED OPTIC FIBER SYSTEM 审中-公开

    公开(公告)号：US20230152150A1

    公开(公告)日：2023-05-18

    申请号：US17987007

    申请日：2022-11-15

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yuheng CHEN ,  Ming-Fang HUANG ,  Ting WANG ,  Jingnan ZHAO

    IPC: G01H9/00 ,  G01P3/36 ,  G06N20/10 ,  G06N3/08

    CPC classification number: G01H9/004 ,  G01P3/36 ,  G06N20/10 ,  G06N3/08

    Abstract: A fiber optic sensing cable located along a side of a paved road and runs parallel to a driving direction is monitored by distributed fiber optic sensing (DFOS) using Rayleigh backscattering generated along the length of the optical sensor fiber cable under dynamic vehicle loads. The interaction of vehicles with roadway locations exhibiting distressed pavement generates unique patterns of localized signals that are identified/distinguished from signals resulting from vehicles operating on roadway exhibiting a smooth pavement surface. Machine learning methods are employed to estimate an overall road surface quality as well as localizing pavement damage. Power spectral density estimation, principal component analysis, support vector machine (SVM) combined with principal component analysis (PCA), local binary pattern (LBP), and convolutional neural network (CNN) are applied to develop the machine learning models.

    FREQUENCY-DRIFT COMPENSATION IN CHIRPED-PULSE-BASED DISTRIBUTED ACOUSTIC SENSING
    505.
    发明公开
    FREQUENCY-DRIFT COMPENSATION IN CHIRPED-PULSE-BASED DISTRIBUTED ACOUSTIC SENSING 审中-公开

    公开(公告)号：US20230146473A1

    公开(公告)日：2023-05-11

    申请号：US17967812

    申请日：2022-10-17

    Applicant: NEC Laboratories America, Inc.

    Inventor： Ezra IP ,  Yue-Kai HUANG

    IPC: G01H9/00 ,  G01D5/353 ,  G01D5/26 ,  H04L27/227

    CPC classification number: G01H9/004 ,  G01D5/268 ,  G01D5/35361 ,  H04L27/2278

    Abstract: Aspects of the present disclosure directed to frequency drift compensation for coded-DAS systems that use chirped pulses as a probe signal. Our inventive approach estimates timing jitter by correlating the amplitude of the estimated Rayleigh impulse response of every frame with a reference frame, and then re-aligns each frame by the estimated timing jitter. As the amount of timing jitter varies within a frame, every frame is divided into blocks where all samples have similar timing jitter, and perform timing jitter estimation and compensation on a block-by-block, frame-by-frame basis using an overlap-and-save method. Tracking of a slowly changing channel is enabled by allowing the reference frame to be periodically updated.

    Deep graph de-noise by differentiable ranking
    506.
    发明授权
    Deep graph de-noise by differentiable ranking 有权

    公开(公告)号：US11645540B2

    公开(公告)日：2023-05-09

    申请号：US16936600

    申请日：2020-07-23

    Applicant: NEC Laboratories America, Inc.

    Inventor： Bo Zong ,  Cheng Zheng ,  Haifeng Chen

    IPC: G06V10/774 ,  G06K9/62 ,  G06F11/07 ,  G06N3/04 ,  G06V30/196

    CPC classification number: G06V10/7747 ,  G06F11/0766 ,  G06K9/6223 ,  G06K9/6257 ,  G06K9/6276 ,  G06N3/0454 ,  G06V30/1988

    Abstract: A method for employing a differentiable ranking based graph sparsification (DRGS) network to use supervision signals from downstream tasks to guide graph sparsification is presented. The method includes, in a training phase, generating node representations by neighborhood aggregation operators, generating sparsified subgraphs by top-k neighbor sampling from a learned neighborhood ranking distribution, feeding the sparsified subgraphs to a task, generating a prediction, and collecting a prediction error to update parameters in the generating and feeding steps to minimize an error, and, in a testing phase, generating node representations by neighborhood aggregation operators related to testing data, generating sparsified subgraphs by top-k neighbor sampling from a learned neighborhood ranking distribution related to the testing data, feeding the sparsified subgraphs related to the testing data to a task, and outputting prediction results to a visualization device.

    DYNAMIC ROAD TRAFFIC NOISE MAPPING USING DISTRIBUTED FIBER OPTIC SENSING (DFOS) OVER TELECOM NETWORK
    507.
    发明申请
    DYNAMIC ROAD TRAFFIC NOISE MAPPING USING DISTRIBUTED FIBER OPTIC SENSING (DFOS) OVER TELECOM NETWORK 有权

    公开(公告)号：US20230125456A1

    公开(公告)日：2023-04-27

    申请号：US17968265

    申请日：2022-10-18

    Applicant: NEC Laboratories America, Inc.

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

    IPC: G08G1/04 ,  G01V1/22 ,  G01V1/00 ,  G01V1/30 ,  G08G1/01

    Abstract: 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.

    DATA FUSION AND ANALYSIS ENGINE FOR VEHICLE SENSORS
    508.
    发明申请
    DATA FUSION AND ANALYSIS ENGINE FOR VEHICLE SENSORS 有权

    公开(公告)号：US20230112441A1

    公开(公告)日：2023-04-13

    申请号：US17961169

    申请日：2022-10-06

    Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

    Inventor： LuAn Tang ,  Yuncong Chen ,  Wei Cheng ,  Zhengzhang Chen ,  Haifeng Chen ,  Yuji Kobayashi ,  Yuxiang Ren

    IPC: B60W40/09 ,  G06K9/62

    Abstract: Systems and methods for data fusion and analysis of vehicle sensor data, including receiving a multiple modality input data stream from a plurality of different types of vehicle sensors, determining latent features by extracting modality-specific features from the input data stream, and aligning a distribution of the latent features of different modalities by feature-level data fusion. Classification probabilities can be determined for the latent features using a fused modality scene classifier. A tree-organized neural network can be trained to determine path probabilities and issue driving pattern judgments, with the tree-organized neural network including a soft tree model and a hard decision leaf. One or more driving pattern judgments can be issued based on a probability of possible driving patterns derived from the modality-specific features.

    SELF-SUPERVISED MULTIMODAL REPRESENTATION LEARNING WITH CASCADE POSITIVE EXAMPLE MINING
    509.
    发明申请
    SELF-SUPERVISED MULTIMODAL REPRESENTATION LEARNING WITH CASCADE POSITIVE EXAMPLE MINING 有权

    公开(公告)号：US20230086023A1

    公开(公告)日：2023-03-23

    申请号：US17940599

    申请日：2022-09-08

    Applicant: NEC Laboratories America, Inc.

    Inventor： Farley Lai ,  Asim Kadav ,  Cheng-En Wu

    IPC: G06V20/40 ,  G06V10/771

    Abstract: A method for model training and deployment includes training, by a processor, a model to learn video representations with a self-supervised contrastive loss by performing progressive training in phases with an incremental number of positive instances from one or more video sequences, resetting the learning rate schedule in each of the phases, and inheriting model weights from a checkpoint from a previous training phase. The method further includes updating the trained model with the self-supervised contrastive loss given multiple positive instances obtained from Cascade K-Nearest Neighbor mining of the one or more video sequences by extracting features in different modalities to compute similarities between the one or more video sequences and selecting a top-k similar instances with features in different modalities. The method also includes fine-tuning the trained model for a downstream task. The method additionally includes deploying the trained model for a target application inference for the downstream task.

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