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    NEURAL POINT PROCESS-BASED EVENT PREDICTION FOR MEDICAL DECISION MAKING
    531.
    发明公开
    NEURAL POINT PROCESS-BASED EVENT PREDICTION FOR MEDICAL DECISION MAKING 审中-公开

    公开(公告)号：US20240186018A1

    公开(公告)日：2024-06-06

    申请号：US18493331

    申请日：2023-10-24

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yuncong Chen ,  Haifeng Chen ,  Zhengzhang Chen ,  Yanchi Liu ,  LuAn Tang

    IPC: G16H50/30

    CPC classification number: G16H50/30

    Abstract: Methods and systems for event prediction include encoding a multivariate time series and a multi-type event sequence using respective transformers and an aggregation network to generate a feature vector. Event prediction is performed using the feature vector to identify a next event to occur within a system. A corrective action is performed responsive to the next event to prevent or mitigate an effect of the next event. The predicted next event can be used in a healthcare context to support decision making by medical professionals with respect to the treatment of a patient. The encoding may include machine learning models to implement the transformers and the aggregation network using deep learning.

    SINGLE TRAINING SEQUENCE FOR NEURAL NETWORK USEABLE FOR MULTI-TASK SCENARIOS
    534.
    发明公开
    SINGLE TRAINING SEQUENCE FOR NEURAL NETWORK USEABLE FOR MULTI-TASK SCENARIOS 审中-公开

    公开(公告)号：US20240160927A1

    公开(公告)日：2024-05-16

    申请号：US18503313

    申请日：2023-11-07

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yumin Suh ,  Samuel Schulter ,  Xiang Yu ,  Abhishek Aich

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: Systems and methods for performing multiple tasks with a single artificial intelligence model that can include training a supernet model for an application by splitting the application into tasks, and splitting the supernet model into subnets. The methods and systems can further assign the tasks computing budgets, and match the tasks to subnets by matching the computing budget of the tasks to the computing capacity of the subnets. Further, the methods and systems can perform the tasks with matching subnets to produce parameters that are used by the supernet to perform the application. The supernet combines all of the task to produce a model for the application and the supernet retains weights for the tasks to be used in subsequent applications.

    UTILITY POLE LOCALIZATION FROM AMBIENT DATA
    537.
    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.

    MULTI-MODALITY DATA AUGMENTATION ENGINE TO IMPROVE RARE DRIVING SCENARIO DETECTION FOR VEHICLE SENSORS
    540.
    发明公开
    MULTI-MODALITY DATA AUGMENTATION ENGINE TO IMPROVE RARE DRIVING SCENARIO DETECTION FOR VEHICLE SENSORS 审中-公开

    公开(公告)号：US20240086586A1

    公开(公告)日：2024-03-14

    申请号：US18464381

    申请日：2023-09-11

    Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

    Inventor： LuAn Tang ,  Shepard Jiang ,  Peng Yuan ,  Yuncong Chen ,  Haifeng Chen ,  Yuji Kobayashi

    IPC: G06F30/20 ,  G06N3/08

    CPC classification number: G06F30/20 ,  G06N3/08

    Abstract: A computer-implemented method for simulating vehicle data and improving driving scenario detection is provided. The method includes retrieving, from vehicle sensors, key parameters from real data of validation scenarios to generate corresponding scenario configurations and descriptions, transferring target scenario descriptions and validation scenario descriptions to target scenario scripts and validation scenario scripts, respectively, to create first raw simulation data pertaining to target scenario descriptions and second raw simulation data pertaining to validation scenario descriptions, training, by an adjuster network, a deep neural network model to minimize differences between the first raw simulation data and the second raw simulation data, refining the first and second raw simulation data of rare driving scenarios to generate rare driving scenario training data, and outputting the rare driving scenario training data to a display screen of a computing device to enable a user to train a scenario detector for an autonomic driving assistant system.

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